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To optimize proteins for particular traits holds great promise for industrial and pharmaceutical purposes. Machine Learning is increasingly applied in this field to predict properties of proteins, thereby guiding the experimental optimization process. A natural question is: How much progress are we making with such predictions, and how important is the choice of regressor and representation? In this paper, we demonstrate that different assessment criteria for regressor performance can lead to dramatically different conclusions, depending on the choice of metric, and how one defines generalization. We highlight the fundamental issues of sample bias in typical regression scenarios and how this can lead to misleading conclusions about regressor performance. Finally, we make the case for the importance of calibrated uncertainty in this domain. Author summary: Supervised machine learning is increasingly used to predict the function and properties of proteins. The performance obtained with these methods relies on a multitude of factors including how data is represented, how observations are distributed, how training is conducted, and how performance is measured. In this paper, we systematically assess the importance of these different components in a protein regression pipeline. We discuss the benefits of using representations extracted from protein language models, the impact of the choice of regression algorithm, and the role of uncertainty. Finally, to avoid misleading performance claims, we stress the need for carefully aligning the train/test setup to reflect the setting in which the prediction algorithm will ultimately be applied. [ABSTRACT FROM AUTHOR]

The heterogeneity of Hepatocellular Carcinoma (HCC) poses a barrier to effective treatment. Stratifying highly heterogeneous HCC into molecular subtypes with similar features is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. This study aims to utilize driver genes to construct HCC subtype models and unravel their molecular mechanisms. Utilizing a novel computational framework, we expanded the initially identified 96 driver genes to 1192 based on mutational aspects and an additional 233 considering driver dysregulation. These genes were subsequently employed as stratification markers for further analyses. A novel multi-omics subtype classification algorithm was developed, leveraging mutation and expression data of the identified stratification genes. This algorithm successfully categorized HCC into two distinct subtypes, CLASS A and CLASS B, demonstrating significant differences in survival outcomes. Integrating multi-omics and single-cell data unveiled substantial distinctions between these subtypes regarding transcriptomics, mutations, copy number variations, and epigenomics. Moreover, our prognostic model exhibited excellent predictive performance in training and external validation cohorts. Finally, a 10-gene classification model for these subtypes identified TTK as a promising therapeutic target with robust classification capabilities. This comprehensive study provides a novel perspective on HCC stratification, offering crucial insights for a deeper understanding of its pathogenesis and the development of promising treatment strategies. Author summary: Dividing highly heterogeneous HCC into molecular subtypes with similar characteristics is crucial for personalized anti-tumor therapies. Although driver genes play pivotal roles in cancer progression, their potential in HCC subtyping has been largely overlooked. In this work, we developed a multi-omics network-based stratification algorithm that utilizes patient mutation data and requires smaller computational resources for subtype assignment. Through this algorithm, we categorized HCC into two subtypes, CLASS A and CLASS B. Using multi-omics and single-cell data, we identified differences between these subtypes in gene expression, methylation, immune infiltration, and other aspects. Beyond subtype characterization, our study established a robust clinical prediction model (https://mike-wang-bjut.shinyapps.io/DynNomapp%5fHCC%5fSutypes/) incorporating subtype information and typical clinical features, enabling precise survival predictions. Finally, we developed a high-performing machine learning classifier for our subtype. Analyzing this classification model and reviewing previous experimental papers, we identified TTK as a potential diagnostic marker and therapeutic target specific to our subtypes. In conclusion, our research offers a novel perspective on HCC stratification, which is crucial for a deeper understanding of its pathogenesis and developing promising treatment strategies. [ABSTRACT FROM AUTHOR]

Recent studies have established that the circadian clock influences onset, progression and therapeutic outcomes in a number of diseases including cancer and heart diseases. Therefore, there is a need for tools to measure the functional state of the molecular circadian clock and its downstream targets in patients. Moreover, the clock is a multi-dimensional stochastic oscillator and there are few tools for analysing it as a noisy multigene dynamical system. In this paper we consider the methodology behind TimeTeller, a machine learning tool that analyses the clock as a noisy multigene dynamical system and aims to estimate circadian clock function from a single transcriptome by modelling the multi-dimensional state of the clock. We demonstrate its potential for clock systems assessment by applying it to mouse, baboon and human microarray and RNA-seq data and show how to visualise and quantify the global structure of the clock, quantitatively stratify individual transcriptomic samples by clock dysfunction and globally compare clocks across individuals, conditions and tissues thus highlighting its potential relevance for advancing circadian medicine. Author summary: The cellular circadian clock consists of an interacting set of genes that through their interactions oscillate throughout the day. This oscillator also responds to external cues so that the genes oscillate in phase with external environmental rhythms. A cell therefore uses its circadian clock to provide its genes with information about the external time. In this way it can coordinate many of the processes taking place in the cell and allocate some of these processes to specific times of the day. It is becoming increasingly clear that the quality of this timing information influences onset progression and outcome in a number of chronic diseases such as cancer. Our aim is therefore to develop a machine-learning tool that can assess how well the clock is working. We want to use this with patients and therefore, for clinical utility, it needs to work with only a single clinical sample and to produce reproducible results that can be clearly interpreted and easily compared. [ABSTRACT FROM AUTHOR]

Quantitative understanding of microbial growth is an essential prerequisite for successful control of pathogens as well as various biotechnology applications. Even though the growth of cell populations has been extensively studied, microbial growth remains poorly characterised at the spatial level. Indeed, even isogenic populations growing at different locations on solid growth medium typically show significant location-dependent variability in growth. Here we show that this variability can be attributed to the initial physiological states of the populations, the interplay between populations interacting with their local environment and the diffusion of nutrients and energy sources coupling the environments. We further show how the causes of this variability change throughout the growth of a population. We use a dual approach, first applying machine learning regression models to discover that location dominates growth variability at specific times, and, in parallel, developing explicit population growth models to describe this spatial effect. In particular, treating nutrient and energy source concentration as a latent variable allows us to develop a mechanistic resource consumer model that captures growth variability across the shared environment. As a consequence, we are able to determine intrinsic growth parameters for each local population, removing confounders common to location-dependent variability in growth. Importantly, our explicit low-parametric model for the environment paves the way for massively parallel experimentation with configurable spatial niches for testing specific eco-evolutionary hypotheses. Author summary: Image-based platforms allow obtaining population size estimates for massively parallel growth experiments on substrate plates at a relatively low cost. However, such population size data has been shown to display a high degree of spatial variability, which occurs even with isogenic populations. Here we first quantified the importance of spatial location on growth variation using a machine learning approach, and then developed spatially aware population growth models to explain the spatial structure of the growth data. Ultimately, we show that a spatial resource consumer model with local microhabitats connected via diffusion, and a parameter capturing the initial physiological state of a population, can fully explain the observed spatial variation in growth while allowing the inference of intrinsic growth parameters of specific populations. This result provides a method for systematic extraction of spatial growth models and paves the way for massively parallel eco-evolutionary experimentation. [ABSTRACT FROM AUTHOR]

Viruses of microbes are ubiquitous biological entities that reprogram their hosts' metabolisms during infection in order to produce viral progeny, impacting the ecology and evolution of microbiomes with broad implications for human and environmental health. Advances in genome sequencing have led to the discovery of millions of novel viruses and an appreciation for the great diversity of viruses on Earth. Yet, with knowledge of only "who is there?" we fall short in our ability to infer the impacts of viruses on microbes at population, community, and ecosystem-scales. To do this, we need a more explicit understanding "who do they infect?" Here, we developed a novel machine learning model (ML), Virus-Host Interaction Predictor (VHIP), to predict virus-host interactions (infection/non-infection) from input virus and host genomes. This ML model was trained and tested on a high-value manually curated set of 8849 virus-host pairs and their corresponding sequence data. The resulting dataset, 'Virus Host Range network' (VHRnet), is core to VHIP functionality. Each data point that underlies the VHIP training and testing represents a lab-tested virus-host pair in VHRnet, from which meaningful signals of viral adaptation to host were computed from genomic sequences. VHIP departs from existing virus-host prediction models in its ability to predict multiple interactions rather than predicting a single most likely host or host clade. As a result, VHIP is able to infer the complexity of virus-host networks in natural systems. VHIP has an 87.8% accuracy rate at predicting interactions between virus-host pairs at the species level and can be applied to novel viral and host population genomes reconstructed from metagenomic datasets. Author summary: The ecology and evolution of microbial communities are deeply influenced by viruses. Metagenomics analysis, the non-targeted sequencing of community genomes, has led to the discovery of millions of novel viruses. Yet, through the sequencing process, only DNA sequences are recovered, begging the question: which microbial hosts do those novel viruses infect? To address this question, we developed a computational tool to allow researchers to predict virus-host interactions from such sequence data. The power of this tool is its use of a high-value, manually curated set of 8849 lab-verified virus-host pairs and their corresponding sequence data. For each pair, we computed signals of coevolution to use as the predictive features in a machine learning model designed to predict interactions between viruses and hosts. The resulting model, Virus-Host Interaction Predictor (VHIP), has an accuracy of 87.8% and can be applied to novel viral and host genomes reconstructed from metagenomic datasets. Because the model considers all possible virus-host pairs, it can resolve complete virus-host interaction networks and supports a new avenue to apply network thinking to viral ecology. [ABSTRACT FROM AUTHOR]

How can inter-individual variability be quantified? Measuring many features per experiment raises the question of choosing them to recapitulate high-dimensional data. Tackling this challenge on spindle elongation phenotypes, we showed that only three typical elongation patterns describe spindle elongation in C. elegans one-cell embryo. These archetypes, automatically extracted from the experimental data using principal component analysis (PCA), accounted for more than 95% of inter-individual variability of more than 1600 experiments across more than 100 different conditions. The two first archetypes were related to spindle average length and anaphasic elongation rate. The third archetype, accounting for 6% of the variability, was novel and corresponded to a transient spindle shortening in late metaphase, reminiscent of kinetochore function-defect phenotypes. Importantly, these three archetypes were robust to the choice of the dataset and were found even considering only non-treated conditions. Thus, the inter-individual differences between genetically perturbed embryos have the same underlying nature as natural inter-individual differences between wild-type embryos, independently of the temperatures. We thus propose that beyond the apparent complexity of the spindle, only three independent mechanisms account for spindle elongation, weighted differently in the various conditions. Interestingly, the spindle-length archetypes covered both metaphase and anaphase, suggesting that spindle elongation in late metaphase is sufficient to predict the late anaphase length. We validated this idea using a machine-learning approach. Finally, given amounts of these three archetypes could represent a quantitative phenotype. To take advantage of this, we set out to predict interacting genes from a seed based on the PCA coefficients. We exemplified this firstly on the role of tpxl-1 whose homolog tpx2 is involved in spindle microtubule branching, secondly the mechanism regulating metaphase length, and thirdly the central spindle players which set the length at anaphase. We found novel interactors not in public databases but supported by recent experimental publications. Author summary: When quantifying the cell, scientists need to accurately measure cell-to-cell variability as it carries unique information about the underlying mechanisms. In this article, we focused on the spindle length as a proxy for correct mitosis. We used the nematode one-cell embryo, an established model organism, to investigate (stem-like) cell divisions. Through a data-only approach of spindle dynamics over time, we automatically extracted the most informative variability descriptors. We recalled two known ones: spindle length and anaphase elongation rate. We uncovered a new one –late-metaphase shortening –present in all conditions. Such a phenotype was previously confined to cells with defective chromosome attachments. These three descriptors account for 95% of variability, suggesting that the complex spindle choreography relies only on a few core mechanisms. Furthermore, we showed that the final spindle length at anaphase, important to set the daughter cell fates, is already determined in late metaphase, despite a complete spindle reassembly between both phases. Interestingly, the same descriptors explain variability in genetically perturbed and non-treated conditions. This suggests that no novel mechanism appears in defective cells. Only mechanism contributions are changing. Finally, we propose a tool to predict genes co-involved in a mechanism from a known gene to support candidate approaches. [ABSTRACT FROM AUTHOR]

Humans tend to give more weight to information confirming their beliefs than to information that disconfirms them. Nevertheless, this apparent irrationality has been shown to improve individual decision-making under uncertainty. However, little is known about this bias' impact on decision-making in a social context. Here, we investigate the conditions under which confirmation bias is beneficial or detrimental to decision-making under social influence. To do so, we develop a Collective Asymmetric Reinforcement Learning (CARL) model in which artificial agents observe others' actions and rewards, and update this information asymmetrically. We use agent-based simulations to study how confirmation bias affects collective performance on a two-armed bandit task, and how resource scarcity, group size and bias strength modulate this effect. We find that a confirmation bias benefits group learning across a wide range of resource-scarcity conditions. Moreover, we discover that, past a critical bias strength, resource abundance favors the emergence of two different performance regimes, one of which is suboptimal. In addition, we find that this regime bifurcation comes with polarization in small groups of agents. Overall, our results suggest the existence of an optimal, moderate level of confirmation bias for decision-making in a social context. Author summary: When we give more weight to information that confirms our existing beliefs, it typically has a negative impact on learning and decision-making. However, our study shows that a moderate confirmation bias can actually improve decision-making when multiple reinforcement learning agents learn together in a social context. This finding has important implications for policymakers who engage in fighting against societal polarization and the spreading of misinformation. It can also inspire the development of artificial, distributed learning algorithms. Based on our research, we recommend not directly targeting confirmation bias but instead focusing on its underlying factors, such as group size, individual incentives, and the interactions between bias and the environment (such as filter bubbles). [ABSTRACT FROM AUTHOR]

Joint species distribution modelling (JSDM) is a widely used statistical method that analyzes combined patterns of all species in a community, linking empirical data to ecological theory and enhancing community-wide prediction tasks. However, fitting JSDMs to large datasets is often computationally demanding and time-consuming. Recent studies have introduced new statistical and machine learning techniques to provide more scalable fitting algorithms, but extending these to complex JSDM structures that account for spatial dependencies or multi-level sampling designs remains challenging. In this study, we aim to enhance JSDM scalability by leveraging high-performance computing (HPC) resources for an existing fitting method. Our work focuses on the Hmsc R-package, a widely used JSDM framework that supports the integration of various dataset types into a single comprehensive model. We developed a GPU-compatible implementation of its model-fitting algorithm using Python and the TensorFlow library. Despite these changes, our enhanced framework retains the original user interface of the Hmsc R-package. We evaluated the performance of the proposed implementation across various model configurations and dataset sizes. Our results show a significant increase in model fitting speed for most models compared to the baseline Hmsc R-package. For the largest datasets, we achieved speed-ups of over 1000 times, demonstrating the substantial potential of GPU porting for previously CPU-bound JSDM software. This advancement opens promising opportunities for better utilizing the rapidly accumulating new biodiversity data resources for inference and prediction. [ABSTRACT FROM AUTHOR]

Neural activity in the cortex exhibits a wide range of firing variability and rich correlation structures. Studies on neural coding indicate that correlated neural variability can influence the quality of neural codes, either beneficially or adversely. However, the mechanisms by which correlated neural variability is transformed and processed across neural populations to achieve meaningful computation remain largely unclear. Here we propose a theory of covariance computation with spiking neurons which offers a unifying perspective on neural representation and computation with correlated noise. We employ a recently proposed computational framework known as the moment neural network to resolve the nonlinear coupling of correlated neural variability with a task-driven approach to constructing neural network models for performing covariance-based perceptual tasks. In particular, we demonstrate how perceptual information initially encoded entirely within the covariance of upstream neurons' spiking activity can be passed, in a near-lossless manner, to the mean firing rate of downstream neurons, which in turn can be used to inform inference. The proposed theory of covariance computation addresses an important question of how the brain extracts perceptual information from noisy sensory stimuli to generate a stable perceptual whole and indicates a more direct role that correlated variability plays in cortical information processing. Author summary: Understanding how the brain represents and processes perceptual information through neuronal firing patterns is at the heart of neuroscience. The prevailing idea suggests that the information is primarily encoded in mean firing rates, whereas correlations among neurons may play a secondary role. However, given that firing variability is ubiquitously observed in cortical neurons, one wonders if correlated noise may play a more central role in neural computation than previously thought. Here, we propose that perceptual information can be encoded in part or even entirely in the correlated variability of spiking neurons. Through a combination of theoretical modeling and machine learning approaches, we construct neural network models capable of processing correlated variability in a task-driven way. We demonstrate that the trained network is able to learn to extract covariance-encoded perceptual information to generate stimulus-selectivity in their mean firing rates, thanks to the nonlinear coupling of statistical moments of their activity. Information-theoretic analysis reveals a near-lossless transfer of perceptual information from the covariance of upstream neurons to the mean firing rate of downstream neurons. Our work offers new insights into the role of correlated variability in cortical processing and hints towards a task-driven paradigm for studying cortical computation with biologically plausible neural network models. [ABSTRACT FROM AUTHOR]

Molecules of the Major Histocompatibility Complex (MHC) present short protein fragments on the cell surface, an important step in T cell immune recognition. MHC-I molecules process peptides from intracellular proteins; MHC-II molecules act in antigen-presenting cells and present peptides derived from extracellular proteins. Here we show that the sequence-dependent energy landscapes of MHC-peptide binding encode class-specific nonlinearities (epistasis). MHC-I has a smooth landscape with global epistasis; the binding energy is a simple deformation of an underlying linear trait. This form of epistasis enhances the discrimination between strong-binding peptides. In contrast, MHC-II has a rugged landscape with idiosyncratic epistasis: binding depends on detailed amino acid combinations at multiple positions of the peptide sequence. The form of epistasis affects the learning of energy landscapes from training data. For MHC-I, a low-complexity problem, we derive a simple matrix model of binding energies that outperforms current models trained by machine learning. For MHC-II, higher complexity prevents learning by simple regression methods. Epistasis also affects the energy and fitness effects of mutations in antigen-derived peptides (epitopes). In MHC-I, large-effect mutations occur predominantly in anchor positions of strong-binding epitopes. In MHC-II, large effects depend on the background epitope sequence but are broadly distributed over the epitope, generating a bigger target for escape mutations due to loss of presentation. Together, our analysis shows how an energy landscape of protein-protein binding constrains the target of escape mutations from T cell immunity, linking the complexity of the molecular interactions to the dynamics of adaptive immune response. Author summary: T cell immunity involves the binding of short peptides to the intracellular MHC recognition machinery. Understanding how the peptide-MHC binding energy depends on the peptide sequence is key to computationally predict immune recognition and immune escape evolution, for example, of pathogens and cancer cells. By analyzing experimental binding affinity data, we find that peptide-MHC interactions are described by nonlinear energy landscapes that depend on the recognition pathway: smooth and easy to learn for MHC class I, rugged and difficult to learn for MHC class II. The different forms of nonlinearity—or epistasis—suggest the most suitable strategy to infer these landscapes. In addition, the nonlinearity has direct implications on the energy and fitness of single mutations, generating distinct distributions of possible targets for immune escape. Together, this work establishes links between biophysical origin, nonlinear structure, learnability from data, and biological implications of peptide-MHC interactions, which can be applicable also to other protein interaction landscapes. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression post-transcriptionally. In animals, this regulation is achieved via base-pairing with partially complementary sequences on mainly 3' UTR region of messenger RNAs (mRNAs). Computational approaches that predict miRNA target interactions (MTIs) facilitate the process of narrowing down potential targets for experimental validation. The availability of new datasets of high-throughput, direct MTIs has led to the development of machine learning (ML) based methods for MTI prediction. To train an ML algorithm, it is beneficial to provide entries from all class labels (i.e., positive and negative). Currently, no high-throughput assays exist for capturing negative examples. Therefore, current ML approaches must rely on either artificially generated or inferred negative examples deduced from experimentally identified positive miRNA-target datasets. Moreover, the lack of uniform standards for generating such data leads to biased results and hampers comparisons between studies. In this comprehensive study, we collected methods for generating negative data for animal miRNAs–target interactions and investigated their impact on the classification of true human MTIs. Our study relies on training ML models on a fixed positive dataset in combination with different negative datasets and evaluating their intra- and cross-dataset performance. As a result, we were able to examine each method independently and evaluate ML models' sensitivity to the methodologies utilized in negative data generation. To achieve a deep understanding of the performance results, we analyzed unique features that distinguish between datasets. In addition, we examined whether one-class classification models that utilize solely positive interactions for training are suitable for the task of MTI classification. We demonstrate the importance of negative data in MTI classification, analyze specific methodological characteristics that differentiate negative datasets, and highlight the challenge of ML models generalizing interaction rules from training to testing sets derived from different approaches. This study provides valuable insights into the computational prediction of MTIs that can be further used to establish standards in the field. Author summary: Gene expression regulation is fundamental for all organisms' development, homeostasis, and environmental adaptation. microRNAs (miRNAs) play a central role in post-transcriptional gene regulation by binding to target mRNAs and repressing their translation or mediating their degradation. Technical challenges in experimental miRNA target identification led to growing interest in computational target prediction. While machine learning (ML) models have shown success in this area, they rely heavily on artificially generated negative examples due to limited experimental data. The diversity of methods for generating negative interactions and the lack of a uniform standardized approach introduce bias and hinder the comparison of results across different studies. In this study, we collected methods for generating negative data for animal miRNAs–target interactions and analyzed their impact on classifying true interactions in humans. Using an ML approach, we evaluated miRNA–target prediction performance within and across negative datasets. We also analyzed unique features distinguishing between the negative datasets to understand the performance results better. Our study shows that negative data is essential for accurately classifying miRNA–target interactions and that ML models struggle to apply what they have learned from the training set when faced with new data derived from different approaches. This study particularly appeals to researchers interested in miRNA–target classification. It emphasizes the need for standardized methods to enhance comparability between studies. [ABSTRACT FROM AUTHOR]

Synthetic lethality (SL) and synthetic viability (SV) are commonly studied genetic interactions in the targeted therapy approach in cancer. In SL, inhibiting either of the genes does not affect the cancer cell survival, but inhibiting both leads to a lethal phenotype. In SV, inhibiting the vulnerable gene makes the cancer cell sick; inhibiting the partner gene rescues and promotes cell viability. Many low and high-throughput experimental approaches have been employed to identify SLs and SVs, but they are time-consuming and expensive. The computational tools for SL prediction involve statistical and machine-learning approaches. Almost all machine learning tools are binary classifiers and involve only identifying SL pairs. Most importantly, there are limited properties known that best describe and discriminate SL from SV. We developed MAGICAL (Multi-class Approach for Genetic Interaction in Cancer via Algorithm Learning), a multi-class random forest based machine learning model for genetic interaction prediction. Network properties of protein derived from physical protein-protein interactions are used as features to classify SL and SV. The model results in an accuracy of ~80% for the training dataset (CGIdb, BioGRID, and SynLethDB) and performs well on DepMap and other experimentally derived reported datasets. Amongst all the network properties, the shortest path, average neighbor2, average betweenness, average triangle, and adhesion have significant discriminatory power. MAGICAL is the first multi-class model to identify discriminatory features of synthetic lethal and viable interactions. MAGICAL can predict SL and SV interactions with better accuracy and precision than any existing binary classifier. Author summary: Targeted therapy aims to selectively target cancer cells without damaging the normal ones. Synthetic lethality is a negative genetic interaction in which alteration of both genes leads to cell death and mediates drug sensitivity. In contrast, synthetic viability is a positive genetic interaction in which gene alteration rescues the cell sickness induced by alteration in the vulnerable gene and promotes cell viability, leading to drug resistance. Hence, identifying these genetic interactions is crucial to fostering selective treatment and improving the patient's health. We have designed MAGICAL, a multi-class classifier for predicting genetic interactions, a machine-learning model that can predict SL and SV based on the network properties. We aim to address how these genetic interactions get affected when the placement of the nodes (genes) in the network changes. As genetic interaction in cancer has a key role in precision oncology/targeted therapy, this work would enable researchers to understand how these interactions foster better treatment. [ABSTRACT FROM AUTHOR]

Circular RNAs (circRNAs) play vital roles in transcription and translation. Identification of circRNA-RBP (RNA-binding protein) interaction sites has become a fundamental step in molecular and cell biology. Deep learning (DL)-based methods have been proposed to predict circRNA-RBP interaction sites and achieved impressive identification performance. However, those methods cannot effectively capture long-distance dependencies, and cannot effectively utilize the interaction information of multiple features. To overcome those limitations, we propose a DL-based model iCRBP-LKHA using deep hybrid networks for identifying circRNA-RBP interaction sites. iCRBP-LKHA adopts five encoding schemes. Meanwhile, the neural network architecture, which consists of large kernel convolutional neural network (LKCNN), convolutional block attention module with one-dimensional convolution (CBAM-1D) and bidirectional gating recurrent unit (BiGRU), can explore local information, global context information and multiple features interaction information automatically. To verify the effectiveness of iCRBP-LKHA, we compared its performance with shallow learning algorithms on 37 circRNAs datasets and 37 circRNAs stringent datasets. And we compared its performance with state-of-the-art DL-based methods on 37 circRNAs datasets, 37 circRNAs stringent datasets and 31 linear RNAs datasets. The experimental results not only show that iCRBP-LKHA outperforms other competing methods, but also demonstrate the potential of this model in identifying other RNA-RBP interaction sites. Author summary: The interaction between circRNAs and RBPs is one of the main activities of circRNAs. CircRNAs participate in the occurrence and development of diseases by interacting with RBPs. Identifying circRNA-RBP interaction sites have become a fundamental step for exploring the role of circRNA in the occurrence and progression of diseases. Many computational methods have been proposed to predict circRNA-RBP interaction sites. Nevertheless, they still have several limitations. For long nucleotide sequence data of circRNA, traditional CNN or LSTM cannot effectively capture long-distance dependencies (relationships between non-adjacent nucleotides in a circRNA). Furthermore, existing methods fail to effectively utilize the interaction information of multiple features, and insufficient consideration of interaction information leads to biased circRNA-RBP interaction relationships. To overcome these limitations, we propose iCRBP-LKHA, based on a large convolutional kernel and hybrid channel-spatial attention for identifying circRNA-RBP interaction sites. We compared its performance with state-of-the-art DL-based methods on 37 circRNAs datasets, 37 circRNAs stringent datasets and 31 linear RNAs datasets. Experimental results not only show that iCRBP-LKHA outperforms competing methods, but also demonstrate the potential of this model in identifying other RNA-RBP interaction sites. [ABSTRACT FROM AUTHOR]

There remains a clinical need for better approaches to rapid drug susceptibility testing in view of the increasing burden of multidrug resistant tuberculosis. Binary susceptibility phenotypes only capture changes in minimum inhibitory concentration when these cross the critical concentration, even though other changes may be clinically relevant. We developed a machine learning system to predict minimum inhibitory concentration from unassembled whole-genome sequencing data for 13 anti-tuberculosis drugs. We trained, validated and tested the system on 10,859 isolates from the CRyPTIC dataset. Essential agreement rates (predicted MIC within one doubling dilution of observed MIC) were above 92% for first-line drugs, 91% for fluoroquinolones and aminoglycosides, and 90% for new and repurposed drugs, albeit with a significant drop in performance for the very few phenotypically resistant isolates in the latter group. To further validate the model in the absence of external MIC datasets, we predicted MIC and converted values to binary for an external set of 15,239 isolates with binary phenotypes, and compare their performance against a previously validated mutation catalogue, the expected performance of existing molecular assays, and World Health Organization Target Product Profiles. The sensitivity of the model on the external dataset was greater than 90% for all drugs except ethionamide, clofazimine and linezolid. Specificity was greater than 95% for all drugs except ethambutol, ethionamide, bedaquiline, delamanid and clofazimine. The proposed system can provide quantitative susceptibility phenotyping to help guide antimicrobial therapy, although further data collection and validation are required before machine learning can be used clinically for all drugs. Author summary: Tuberculosis is a leading cause of morbidity and mortality globally, killing over 1.5 million people each year. Tuberculosis drug susceptibility testing is used to determine which antibiotics should be used to manage the disease, particularly in view of the rise in multidrug resistant tuberculosis. The current standard for testing, binary phenotypes, only capture changes in minimum inhibitory concentration when these cross a critical concentration, even though other changes may be clinically relevant. We developed a machine learning system to predict minimum inhibitory concentration directly from sequencing data for 13 antibiotics. We find that the model has an accuracy of 92% for first-line drugs, 91% for fluoroquinolones and aminoglycosides, and 90% for new and repurposed drugs, with a significant drop in performance for resistant isolates in the latter group. We validated the model on an external dataset with binary phenotypes. The sensitivity of the model on the external dataset was greater than 90% for all drugs except ethionamide, clofazimine and linezolid. Specificity was greater than 95% for all drugs except ethambutol, ethionamide, bedaquiline, delamanid and clofazimine. The proposed system could help guide antimicrobial therapy. However, further data and validation are required before machine learning can be used clinically for all drugs. [ABSTRACT FROM AUTHOR]

Bacteriophages (phages) are viruses that infect bacteria. Many of them produce specific enzymes called depolymerases to break down external polysaccharide structures. Accurate annotation and domain identification of these depolymerases are challenging due to their inherent sequence diversity. Hence, we present DepoScope, a machine learning tool that combines a fine-tuned ESM-2 model with a convolutional neural network to identify depolymerase sequences and their enzymatic domains precisely. To accomplish this, we curated a dataset from the INPHARED phage genome database, created a polysaccharide-degrading domain database, and applied sequential filters to construct a high-quality dataset, which is subsequently used to train DepoScope. Our work is the first approach that combines sequence-level predictions with amino-acid-level predictions for accurate depolymerase detection and functional domain identification. In that way, we believe that DepoScope can greatly enhance our understanding of phage-host interactions at the level of depolymerases. [ABSTRACT FROM AUTHOR]

This article discusses the importance of incorporating ethics and societal impact considerations into data science research. It outlines 10 rules for building and maintaining a responsible data science workflow, including considering potential harms, questioning data inputs for biases and privacy concerns, and maintaining transparency and diversity in research teams. The article emphasizes the need for transparency, reproducibility, and documentation in data science research, as well as the use of open-source tools for detecting biases and unfairness. It acknowledges the challenges involved in implementing a responsible workflow but highlights the benefits for both the research team and the wider community. [Extracted from the article]

The classification of B cell lymphomas—mainly based on light microscopy evaluation by a pathologist—requires many years of training. Since the B cell receptor (BCR) of the lymphoma clonotype and the microenvironmental immune architecture are important features discriminating different lymphoma subsets, we asked whether BCR repertoire next-generation sequencing (NGS) of lymphoma-infiltrated tissues in conjunction with machine learning algorithms could have diagnostic utility in the subclassification of these cancers. We trained a random forest and a linear classifier via logistic regression based on patterns of clonal distribution, VDJ gene usage and physico-chemical properties of the top-n most frequently represented clonotypes in the BCR repertoires of 620 paradigmatic lymphoma samples—nodular lymphocyte predominant B cell lymphoma (NLPBL), diffuse large B cell lymphoma (DLBCL) and chronic lymphocytic leukemia (CLL)—alongside with 291 control samples. With regard to DLBCL and CLL, the models demonstrated optimal performance when utilizing only the most prevalent clonotype for classification, while in NLPBL—that has a dominant background of non-malignant bystander cells—a broader array of clonotypes enhanced model accuracy. Surprisingly, the straightforward logistic regression model performed best in this seemingly complex classification problem, suggesting linear separability in our chosen dimensions. It achieved a weighted F1-score of 0.84 on a test cohort including 125 samples from all three lymphoma entities and 58 samples from healthy individuals. Together, we provide proof-of-concept that at least the 3 studied lymphoma entities can be differentiated from each other using BCR repertoire NGS on lymphoma-infiltrated tissues by a trained machine learning model. Author summary: Lymphoma, a complex group of malignant blood cancers, poses a significant diagnostic challenge due to its diverse subtypes. Yet, precise classification is crucial for tailored treatment. In our research, we developed a machine learning algorithm and conducted comprehensive validation to discern distinct B cell lymphoma subtypes. We therefore leveraged B cell repertoires of lymphoma-infiltrated tissue, as ascertained through next-generation sequencing. Our data offers three key insights: We detail the creation and training of our machine learning algorithm, explaining how we selected features and designed the model. We demonstrate the algorithm's diagnostic precision using sequencing data from a test-set of patient samples. Moreover, through a deep dive into the most distinguishing aspects of our algorithm, we unveil distinctive disease-related patterns present within the malignant B cell and its surrounding environment. This analysis showed that both the malignant lymphoma cell, but also healthy bystander immune cells contribute to the distinctive architecture that characterizes a specific lymphoma subtype. We hope our work will contribute towards creating tools to diagnose lymphoma more easily and accurately ultimately leading to better outcomes for patients with this type of cancer. [ABSTRACT FROM AUTHOR]

Computational fluid dynamics (CFD) can be used for non-invasive evaluation of hemodynamics. However, its routine use is limited by labor-intensive manual segmentation, CFD mesh creation, and time-consuming simulation. This study aims to train a deep learning model to both generate patient-specific volume-meshes of the pulmonary artery from 3D cardiac MRI data and directly estimate CFD flow fields. This proof-of-concept study used 135 3D cardiac MRIs from both a public and private dataset. The pulmonary arteries in the MRIs were manually segmented and converted into volume-meshes. CFD simulations were performed on ground truth meshes and interpolated onto point-point correspondent meshes to create the ground truth dataset. The dataset was split 110/10/15 for training, validation, and testing. Image2Flow, a hybrid image and graph convolutional neural network, was trained to transform a pulmonary artery template to patient-specific anatomy and CFD values, taking a specific inlet velocity as an additional input. Image2Flow was evaluated in terms of segmentation, and the accuracy of predicted CFD was assessed using node-wise comparisons. In addition, the ability of Image2Flow to respond to increasing inlet velocities was also evaluated. Image2Flow achieved excellent segmentation accuracy with a median Dice score of 0.91 (IQR: 0.86–0.92). The median node-wise normalized absolute error for pressure and velocity magnitude was 11.75% (IQR: 9.60–15.30%) and 9.90% (IQR: 8.47–11.90), respectively. Image2Flow also showed an expected response to increased inlet velocities with increasing pressure and velocity values. This proof-of-concept study has shown that it is possible to simultaneously perform patient-specific volume-mesh based segmentation and pressure and flow field estimation using Image2Flow. Image2Flow completes segmentation and CFD in ~330ms, which is ~5000 times faster than manual methods, making it more feasible in a clinical environment. Author summary: Computational fluid dynamics is an engineering tool that can be used in a clinical setting to non-invasively model blood flow through blood vessels, such as the pulmonary artery. This information can be used to inform treatment planning for patients, especially those with cardiovascular conditions. However, its routine use is limited by a labor-intensive process, requiring substantial expertise and computational resources. Recently, machine learning has offered solutions to automate parts of this process, but no single model has addressed the entire workflow. Therefore, we created Image2Flow, a machine learning model capable of generating a patient-specific representation of a pulmonary artery while predicting blood flow through the vessel. Image2Flow can generate highly accurate pulmonary artery representations and reasonably accurate blood flow predictions. Notably, it performs this whole process in 330ms, which is ~5000x faster than the manual methods. With its speed and accuracy, Image2Flow holds substantial promise for facilitating computational fluid dynamics in clinical settings. [ABSTRACT FROM AUTHOR]

Tumor microenvironments (TMEs) contain vast amounts of information on patient's cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs between patient groups, as well as determining the extent to which this information can predict outcomes such as patient survival or treatment success with emerging immunotherapies, is of great interest. Moreover, in the face of a large number of cell interactions to consider, we often wish to identify specific interactions that are useful in making such predictions. We present an approach to achieve these goals based on summarizing spatial relationships in the TME using spatial K functions, and then applying functional data analysis and random forest models to both predict outcomes of interest and identify important spatial relationships. This approach is shown to be effective in simulation experiments at both identifying important spatial interactions while also controlling the false discovery rate. We further used the proposed approach to interrogate two real data sets of Multiplexed Ion Beam Images of TMEs in triple negative breast cancer and lung cancer patients. The methods proposed are publicly available in a companion R package funkycells. Author summary: Spatial data on the tumor microenvironment (TME) are becoming more prevalent. Existing methods to interrogate such data often have several limitations: (1) they can rely on estimating the spatial relationships among cells by examining simple counts of cells within a single radius, (2) they may not come with ways to evaluate the statistical significance of any findings, or (3) they model individual interactions independently of other interactions. Our approach leverages techniques in spatial statistics and uses a benchmark ensemble machine learning method to address each of these deficiencies; it (1) uses K functions to encode the relative densities of cells over all radii up to a user-selected maximum radius, (2) employs permutation and cross-validation to evaluate the statistical significance of any findings on the spatial interactions in the TME, and (3) models multiple interactions simultaneously. Our approach is freely available with an R implementation called funkycells. In the analysis of two real data sets, we have seen that the method performs well, and gives the expected results. We think this will be a robust tool for researchers looking to interrogate TME data. [ABSTRACT FROM AUTHOR]

Immunization through repeated direct venous inoculation of Plasmodium falciparum (Pf) sporozoites (PfSPZ) under chloroquine chemoprophylaxis, using the PfSPZ Chemoprophylaxis Vaccine (PfSPZ-CVac), induces high-level protection against controlled human malaria infection (CHMI). Humoral and cellular immunity contribute to vaccine efficacy but only limited information about the implicated Pf-specific antigens is available. Here, we examined Pf-specific antibody profiles, measured by protein arrays representing the full Pf proteome, of 40 placebo- and PfSPZ-immunized malaria-naïve volunteers from an earlier published PfSPZ-CVac dose-escalation trial. For this purpose, we both utilized and adapted supervised machine learning methods to identify predictive antibody profiles at two different time points: after immunization and before CHMI. We developed an adapted multitask support vector machine (SVM) approach and compared it to standard methods, i.e. single-task SVM, regularized logistic regression and random forests. Our results show, that the multitask SVM approach improved the classification performance to discriminate the protection status based on the underlying antibody-profiles while combining time- and dose-dependent data in the prediction model. Additionally, we developed the new fEature diStance exPlainabilitY (ESPY) method to quantify the impact of single antigens on the non-linear multitask SVM model and make it more interpretable. In conclusion, our multitask SVM model outperforms the studied standard approaches in regard of classification performance. Moreover, with our new explanation method ESPY, we were able to interpret the impact of Pf-specific antigen antibody responses that predict sterile protective immunity against CHMI after immunization. The identified Pf-specific antigens may contribute to a better understanding of immunity against human malaria and may foster vaccine development. Author summary: Developing an effective malaria vaccine is challenging. Malaria is a life-threatening disease caused by the plasmodium parasite, which has a complex multi-stage life-cycle and expresses several thousand proteins in a highly coordinated manner. To date, our understanding of the immune mechanisms mediating protection against Plasmodium falciparum (Pf) is incomplete. Proteome microarrays have been used earlier by our clinical collaboration partners to identify Pf-specific antibody profiles of malaria-naïve volunteers during immunization with attenuated Pf sporozoites (PfSPZ). We reused this data to compare the ability of three supervised machine learning methods to identify predictive antibody profiles after immunization and before controlled human malaria infection (CHMI). We adapted a multitask support vector machine (SVM) approach to analyze time-dependent Pf-induced antibody profiles from several time points in a single prediction model. Our multitask SVM approach outperforms the studied standard approaches in classification performance. Additionally, we developed a new explanation method, named fEature diStance exPlainabilitY (ESPY), to interpret the impact of Pf-specific antigens. We applied ESPY on the multitask SVM model and identified diverse Pf-specific antigen sets after immunization and before CHMI. Furthermore, we showed that the identified Pf-induced antibody profiles vary among protected and non-protected individuals who had been exposed to different doses of PfSPZ. [ABSTRACT FROM AUTHOR]

Multi-factor screenings are commonly used in diverse applications in medicine and bioengineering, including optimizing combination drug treatments and microbiome engineering. Despite the advances in high-throughput technologies, large-scale experiments typically remain prohibitively expensive. Here we introduce a machine learning platform, structure-augmented regression (SAR), that exploits the intrinsic structure of each biological system to learn a high-accuracy model with minimal data requirement. Under different environmental perturbations, each biological system exhibits a unique, structured phenotypic response. This structure can be learned based on limited data and once learned, can constrain subsequent quantitative predictions. We demonstrate that SAR requires significantly fewer data comparing to other existing machine-learning methods to achieve a high prediction accuracy, first on simulated data, then on experimental data of various systems and input dimensions. We then show how a learned structure can guide effective design of new experiments. Our approach has implications for predictive control of biological systems and an integration of machine learning prediction and experimental design. Author summary: Using limited data to predict how biological systems respond to combinations of perturbations is important for better understanding of biological systems and for practical applications. Here we present a new method, structure-augmented regression, that efficiently learns biological response landscapes from sparse measurements. Our method exploits the property that many biological response landscapes have distinct lower-dimensional structures, which can be learned first to assist the subsequent quantitative predictions. We demonstrate that our algorithm outperforms existing algorithms on simulated and experimental data of various biological systems and dimensions. We further exploit the learned structure by suggesting new experiments that refines the learned structure to further improve the prediction accuracy. By integrating machine learning with experimental design, our approach has implications for the predictive control of biological systems. [ABSTRACT FROM AUTHOR]

Unit activity in particular deep neural networks (DNNs) are remarkably similar to the neuronal population responses to static images along the primate ventral visual cortex. Linear combinations of DNN unit activities are widely used to build predictive models of neuronal activity in the visual cortex. Nevertheless, prediction performance in these models is often investigated on stimulus sets consisting of everyday objects under naturalistic settings. Recent work has revealed a generalization gap in how predicting neuronal responses to synthetically generated out-of-distribution (OOD) stimuli. Here, we investigated how the recent progress in improving DNNs' object recognition generalization, as well as various DNN design choices such as architecture, learning algorithm, and datasets have impacted the generalization gap in neural predictivity. We came to a surprising conclusion that the performance on none of the common computer vision OOD object recognition benchmarks is predictive of OOD neural predictivity performance. Furthermore, we found that adversarially robust models often yield substantially higher generalization in neural predictivity, although the degree of robustness itself was not predictive of neural predictivity score. These results suggest that improving object recognition behavior on current benchmarks alone may not lead to more general models of neurons in the primate ventral visual cortex. Author summary: Inspired by the neural circuits of the brain, deep neural networks (DNN) have been steadily improving in their ability to perform foundational visual tasks such as object recognition. Whereas, early models struggled with generalization to abstract visual domains such as line drawings and cartoons, recent advancement have approached near-human recognition capabilities. Moreover, the unit activity in these networks exhibit strong similarities with the activity of single-unit recordings along the primate ventral visual cortex. This capability of DNNs has provided visual neuroscientists with precise models for exploring the neural underpinnings of object recognition. Our research probes whether enhancements in neural networks' recognition of out-of-distribution objects correlate with improved predictability of brain activity in the visual cortex of monkeys to synthetic stimuli. We found that the out of distribution object recognition performance on natural image datasets is not a reliable measure of neural predictivity. However, DNN models that were trained to be more resilient to adversarially generated noise patterns as well as DNN ensembles, consistently yielded better generalization in neural predictivity. Altogether, our results suggest that improving object recognition behaviour on current benchmarks alone may not lead to more general models of neurons in the primate ventral visual cortex. [ABSTRACT FROM AUTHOR]

Animals can learn in real-life scenarios where rewards are often only available when a goal is achieved. This 'distal' or 'sparse' reward problem remains a challenge for conventional reinforcement learning algorithms. Here we investigate an algorithm for learning in such scenarios, inspired by the possibility that axo-axonal gap junction connections, observed in neural circuits with parallel fibres such as the insect mushroom body, could form a resistive network. In such a network, an active node represents the task state, connections between nodes represent state transitions and their connection to actions, and current flow to a target state can guide decision making. Building on evidence that gap junction weights are adaptive, we propose that experience of a task can modulate the connections to form a graph encoding the task structure. We demonstrate that the approach can be used for efficient reinforcement learning under sparse rewards, and discuss whether it is plausible as an account of the insect mushroom body. Author summary: Learning in situations where reward is only rarely encountered is difficult. It is hard to discover the right sequence of actions when most actions, most of the time, provide no apparent progress towards a goal. Inspired by a neural circuit in the insect brain, and using direct electrical connections between neurons as well as synaptic connections, we present a new algorithm for learning. The model represents the states of the world with nodes and an electrical connection between two nodes is strengthened when the two corresponding states occur consecutively. The connections between nodes can also become associated to output actions that correlate with (hence are assumed to cause) transitions between states. When a particular goal is chosen or associated with a reward, for example, the target location in a navigation task, a flow of electrical current through the nodes will find the shortest path from the present state to the goal state and trigger the appropriate actions. [ABSTRACT FROM AUTHOR]

The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework that employs a multimodal Transformer Network to simultaneously process protein amino acid sequences and small molecule strings in the same input. This approach facilitates the exchange of all relevant information between the two molecule types during the computation of their numerical representations, allowing the model to account for their structural and functional interactions. Our final model combines gradient boosting predictions based on the resulting multimodal Transformer Network with independent predictions based on separate deep learning representations of the proteins and small molecules. The resulting predictions outperform recently published state-of-the-art models for predicting protein-small molecule interactions across three diverse tasks: predicting kinase inhibitions; inferring potential substrates for enzymes; and predicting Michaelis constants KM. The Python code provided can be used to easily implement and improve machine learning predictions involving arbitrary protein-small molecule interactions. Author summary: Understanding how proteins interact with small molecules, such as drugs, is critical to advancing medical, biological, and biotechnological research. Our work introduces ProSmith, a machine learning framework that improves the prediction of protein-small molecule interactions. Protein-small molecule interactions can be predicted by using numerical representations of proteins and small molecules as input to machine learning prediction models. Previous methods typically generated separate numerical representations for the proteins and small molecules without considering their interactions. ProSmith, however, combines both protein sequence and small molecule structural information in the input of a single multimodal Transformer Network to generate a joint numerical representation. Unlike previous methods, this allows for a comprehensive exchange of information between protein and small molecule, capturing the complex relationships and interactions between these two types of molecules. ProSmith successfully predicts several biological interactions, including kinase inhibitions, potential enzyme-substrate pairs, and enzyme kinetic parameters KM. We provide Python code that can be easily adapted to improve predictions for any protein-small molecule interaction. [ABSTRACT FROM AUTHOR]

Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors before surgery while simultaneously allowing for controlled evaluation of biomarkers, toxicity, and suppression of distant (occult) metastatic disease. Yet neoadjuvant clinical trials are rarely preceded by preclinical testing involving neoadjuvant treatment, surgery, and post-surgery monitoring of the disease. Here we used a mouse model of spontaneous metastasis occurring after surgical removal of orthotopically implanted primary tumors to develop a predictive mathematical model of neoadjuvant treatment response to sunitinib, a receptor tyrosine kinase inhibitor (RTKI). Treatment outcomes were used to validate a novel mathematical kinetics-pharmacodynamics model predictive of perioperative disease progression. Longitudinal measurements of presurgical primary tumor size and postsurgical metastatic burden were compiled using 128 mice receiving variable neoadjuvant treatment doses and schedules (released publicly at https://zenodo.org/records/10607753). A non-linear mixed-effects modeling approach quantified inter-animal variabilities in metastatic dynamics and survival, and machine-learning algorithms were applied to investigate the significance of several biomarkers at resection as predictors of individual kinetics. Biomarkers included circulating tumor- and immune-based cells (i.e., circulating tumor cells and myeloid-derived suppressor cells) as well as immunohistochemical tumor proteins (i.e., CD31 and Ki67). Our computational simulations show that neoadjuvant RTKI treatment inhibits primary tumor growth but has little efficacy in preventing (micro)-metastatic disease progression after surgery and treatment cessation. Machine learning algorithms that included support vector machines, random forests, and artificial neural networks, confirmed a lack of definitive biomarkers, which shows the value of preclinical modeling studies to identify potential failures that should be avoided clinically. Author summary: Using simulations from a mechanistic mathematical model compared with preclinical data from surgical metastasis models, we found that anti-tumor effects of neoadjuvant receptor tyrosine kinase inhibitor treatment can differ between the primary tumor and metastases in the perioperative setting. Model simulations with variable drug doses and scheduling of neoadjuvant treatment revealed a contrasting impact on initial primary tumor debulking and metastatic outcomes long after treatment has stopped and the primary tumor has been surgically removed. Using machine-learning algorithms, we identified the limited power of several circulating cellular and molecular biomarkers in predicting metastatic outcomes, uncovering a potential fast-track strategy for assessing future clinical biomarkers by pairing patient studies with identical studies in mice. [ABSTRACT FROM AUTHOR]

The use of deep learning (DL) is steadily gaining traction in scientific challenges such as cancer research. Advances in enhanced data generation, machine learning algorithms, and compute infrastructure have led to an acceleration in the use of deep learning in various domains of cancer research such as drug response problems. In our study, we explored tree-based models to improve the accuracy of a single drug response model and demonstrate that tree-based models such as XGBoost (eXtreme Gradient Boosting) have advantages over deep learning models, such as a convolutional neural network (CNN), for single drug response problems. However, comparing models is not a trivial task. To make training and comparing CNNs and XGBoost more accessible to users, we developed an open-source library called UNNT (A novel Utility for comparing Neural Net and Tree-based models). The case studies, in this manuscript, focus on cancer drug response datasets however the application can be used on datasets from other domains, such as chemistry. Author summary: Advancement in data science, machine learning (ML), and artificial intelligence (AI) methods has enabled extraction of meaningful information from large and complex datasets that has assisted in better understanding, diagnosing, and treating cancer. The understanding of the drug response domain in cancer research has been accelerated with developing ML models to aid in predicting the effectiveness of the drugs based on a specific genomic molecular feature. In this study we developed a novel robust framework called UNNT (A novel Utility for comparing Neural Net and Tree-based models) that trains and compares deep learning method such as CNN and tree-based method such as XGBoost on the user input dataset. We applied this software to single drug response problem in cancer to identify the best performing ML method based on the National Cancer Institute 60 (NCI60) dataset. In addition, we studied the computational aspects of training each of these models where our results show that neither is evidently superior on both CPUs and GPUs while training. This shows that when both models have similar error rates for a dataset the hardware available determines the model choice for training. [ABSTRACT FROM AUTHOR]

Neural Cellular Automata (NCA) are a powerful combination of machine learning and mechanistic modelling. We train NCA to learn complex dynamics from time series of images and PDE trajectories. Our method is designed to identify underlying local rules that govern large scale dynamic emergent behaviours. Previous work on NCA focuses on learning rules that give stationary emergent structures. We extend NCA to capture both transient and stable structures within the same system, as well as learning rules that capture the dynamics of Turing pattern formation in nonlinear Partial Differential Equations (PDEs). We demonstrate that NCA can generalise very well beyond their PDE training data, we show how to constrain NCA to respect given symmetries, and we explore the effects of associated hyperparameters on model performance and stability. Being able to learn arbitrary dynamics gives NCA great potential as a data driven modelling framework, especially for modelling biological pattern formation. Author summary: Pattern formation is ubiquitous in biological systems, across many length and time scales—from vegetation stripes in deserts, to the spots of a leopard's skin, and the fine detail of stem cell differentiation in an embryo. While many simple rules that create complex patterns are known, reverse engineering the mechanisms responsible from an observed pattern has generally remained a difficult problem. In this work we build on the connections between machine learning, cellular automata, and partial differential equations, to create models that can learn the underlying mechanisms that yield any desired emergent pattern. To do this we consider Neural Cellular Automata (a mix of neural networks and cellular automata). We describe how they are built and trained, and we show that they are capable of reproducing a wide range of complex emergent behaviours. Previous work focuses on learning stationary patterns, but we focus on patterns that evolve in time. We show these models can learn classic Turing patterns (biochemical models of spot and stripe formation), as well as learning to morph through an arbitrary sequence of images. We believe this hybrid between machine learning and mechanistic modelling has great potential for modelling biological growth, regeneration, and pattern formation. [ABSTRACT FROM AUTHOR]

Biomedical texts provide important data for investigating drug-drug interactions (DDIs) in the field of pharmacovigilance. Although researchers have attempted to investigate DDIs from biomedical texts and predict unknown DDIs, the lack of accurate manual annotations significantly hinders the performance of machine learning algorithms. In this study, a new DDI prediction framework, Subgraph Enhance model, was developed for DDI (SubGE-DDI) to improve the performance of machine learning algorithms. This model uses drug pairs knowledge subgraph information to achieve large-scale plain text prediction without many annotations. This model treats DDI prediction as a multi-class classification problem and predicts the specific DDI type for each drug pair (e.g. Mechanism, Effect, Advise, Interact and Negative). The drug pairs knowledge subgraph was derived from a huge drug knowledge graph containing various public datasets, such as DrugBank, TwoSIDES, OffSIDES, DrugCentral, EntrezeGene, SMPDB (The Small Molecule Pathway Database), CTD (The Comparative Toxicogenomics Database) and SIDER. The SubGE-DDI was evaluated from the public dataset (SemEval-2013 Task 9 dataset) and then compared with other state-of-the-art baselines. SubGE-DDI achieves 83.91% micro F1 score and 84.75% macro F1 score in the test dataset, outperforming the other state-of-the-art baselines. These findings show that the proposed drug pairs knowledge subgraph-assisted model can effectively improve the prediction performance of DDIs from biomedical texts. Author summary: Drug-drug interactions occur when two or more drugs react with each other, potentially leading to adverse drug reactions that can manifest as toxicity, reduced treatment efficacy, and, in extreme cases, patient mortality. With the growing number of approved drugs, detecting negative drug-drug interactions has become a crucial concern in pharmacovigilance. However, manually mining these interactions from the vast and rapidly expanding pool of published biomedical texts, such as clinical trial reports and literature, is arduous and challenging. To address this issue, various machine learning methods have been developed and applied to text mining for both known and unknown drug-drug interactions. Most of these methods primarily predict interactions by considering only drug molecular properties and text characters, neglecting valuable information about interactions between different entities, such as drugs, pathways, and side effects. In response, our approach seeks to enhance the performance of drug-drug interaction prediction by leveraging knowledge graphs constructed from a variety of biomedical entities. Our experimental results validate this approach, demonstrating that incorporating interactive information from knowledge graphs leads to the development of a more efficient model for predicting drug-drug interactions. [ABSTRACT FROM AUTHOR]

Animal psychophysics can generate rich behavioral datasets, often comprised of many 1000s of trials for an individual subject. Gradient-boosted models are a promising machine learning approach for analyzing such data, partly due to the tools that allow users to gain insight into how the model makes predictions. We trained ferrets to report a target word's presence, timing, and lateralization within a stream of consecutively presented non-target words. To assess the animals' ability to generalize across pitch, we manipulated the fundamental frequency (F0) of the speech stimuli across trials, and to assess the contribution of pitch to streaming, we roved the F0 from word token-to-token. We then implemented gradient-boosted regression and decision trees on the trial outcome and reaction time data to understand the behavioral factors behind the ferrets' decision-making. We visualized model contributions by implementing SHAPs feature importance and partial dependency plots. While ferrets could accurately perform the task across all pitch-shifted conditions, our models reveal subtle effects of shifting F0 on performance, with within-trial pitch shifting elevating false alarms and extending reaction times. Our models identified a subset of non-target words that animals commonly false alarmed to. Follow-up analysis demonstrated that the spectrotemporal similarity of target and non-target words rather than similarity in duration or amplitude waveform was the strongest predictor of the likelihood of false alarming. Finally, we compared the results with those obtained with traditional mixed effects models, revealing equivalent or better performance for the gradient-boosted models over these approaches. Author summary: The sorts of listening challenges faced by real-world listeners are rarely captured by most laboratory-based auditory paradigms, particularly those testing animal models. However, many labs are attempting to utilize more realistic experiments, and more complicated behavioral paradigms require more sophisticated approaches to analyzing the resulting data. Here, we used a new behavioral paradigm to test the ability of ferret listeners to identify target speech sounds and assess their ability to generalize across changes in pitch. To make sense of the resulting dataset, we used machine learning to understand how trained ferrets perform this task. Gradient-boosted regression and decision trees are well-established machine learning methods that do not require users to predetermine interaction effects and are accompanied by visualization tools that allow insights into how multiple factors ultimately shape behavior. We compare the use of gradient-boosted models to more standard regression approaches and that this machine learning approach is ideal for analyzing behavioral data in animal models. [ABSTRACT FROM AUTHOR]

Early identification of safe and efficacious disease targets is crucial to alleviating the tremendous cost of drug discovery projects. However, existing experimental methods for identifying new targets are generally labor-intensive and failure-prone. On the other hand, computational approaches, especially the machine learning-based frameworks, have shown remarkable application potential in drug discovery. In this work, we propose Progeni, a novel machine learning-based framework for target identification. In addition to fully exploiting the known heterogeneous biological networks from various sources, Progeni integrates literature evidence about the relations between biological entities to construct a probabilistic knowledge graph. Graph neural networks are then employed in Progeni to learn the feature embeddings of biological entities to facilitate the identification of biologically relevant target candidates. A comprehensive evaluation of Progeni demonstrated its superior predictive power over the baseline methods on the target identification task. In addition, our extensive tests showed that Progeni exhibited high robustness to the negative effect of exposure bias, a common phenomenon in recommendation systems, and effectively identified new targets that can be strongly supported by the literature. Moreover, our wet lab experiments successfully validated the biological significance of the top target candidates predicted by Progeni for melanoma and colorectal cancer. All these results suggested that Progeni can identify biologically effective targets and thus provide a powerful and useful tool for advancing the drug discovery process. Author summary: The selection of a disease target, a biological entity with which potential drugs interact to treat diseases, is often the first step of a drug discovery project. Known to be extremely costly, drug discovery projects spend tremendous resources on failed drug instances, most of which result from inadequate choices of targets. To help address the costliness problem of drug discovery, we developed a machine learning-based framework that identifies biologically effective targets using a probabilistic knowledge graph built from both biological network data and literature evidence. Our method not only outperforms state-of-the-art baseline methods on the target prediction task, but can also identify targets with high biological relevance, as shown by the strong support of the literature for the predicted target candidates and wet lab experiments that validate the significance of the predicted target candidates for melanoma and colorectal cancer, These results suggest that our method can identify effective targets and therefore benefit drug discovery. [ABSTRACT FROM AUTHOR]

A key element for successful blood transfusion is compatibility of the patient and donor red blood cell (RBC) antigens. Precise antigen matching reduces the risk for immunization and other adverse transfusion outcomes. RBC antigens are encoded by specific genes, which allows developing computational methods for determining antigens from genomic data. We describe here a classification method for determining RBC antigens from genotyping array data. Random forest models for 39 RBC antigens in 14 blood group systems and for human platelet antigen (HPA)-1 were trained and tested using genotype and RBC antigen and HPA-1 typing data available for 1,192 blood donors in the Finnish Blood Service Biobank. The algorithm and models were further evaluated using a validation cohort of 111,667 Danish blood donors. In the Finnish test data set, the median (interquartile range [IQR]) balanced accuracy for 39 models was 99.9 (98.9–100)%. We were able to replicate 34 out of 39 Finnish models in the Danish cohort and the median (IQR) balanced accuracy for classifications was 97.1 (90.1–99.4)%. When applying models trained with the Danish cohort, the median (IQR) balanced accuracy for the 40 Danish models in the Danish test data set was 99.3 (95.1–99.8)%. The RBC antigen and HPA-1 prediction models demonstrated high overall accuracies suitable for probabilistic determination of blood groups and HPA-1 at biobank-scale. Furthermore, population-specific training cohort increased the accuracies of the models. This stand-alone and freely available method is applicable for research and screening for antigen-negative blood donors. Author summary: Blood transfusion is one of the most common clinical procedures in the hospitals and the key element for safe transfusion is compatibility between the recipient and donor red blood cell antigens. Precise antigen matching reduces the risk for sensitization and other adverse transfusion outcomes. Here we describe a stand-alone and freely available random forest classification method and models for determining red blood cell and human platelet antigens from array technology-based genotyping data. We investigate the performance of models trained with Finnish blood donor biobank data and further validate the method with a large Danish cohort. The results demonstrate high overall accuracy, and the method is suitable for biobank-scale research and screening of antigen-negative donors. The implementation is possible in the local computing environment without sensitive data uploads and requires only a moderate level of bioinformatic skills. [ABSTRACT FROM AUTHOR]

Post-translational modifications (PTMs) of proteins play a vital role in their function and stability. These modifications influence protein folding, signaling, protein-protein interactions, enzyme activity, binding affinity, aggregation, degradation, and much more. To date, over 400 types of PTMs have been described, representing chemical diversity well beyond the genetically encoded amino acids. Such modifications pose a challenge to the successful design of proteins, but also represent a major opportunity to diversify the protein engineering toolbox. To this end, we first trained artificial neural networks (ANNs) to predict eighteen of the most abundant PTMs, including protein glycosylation, phosphorylation, methylation, and deamidation. In a second step, these models were implemented inside the computational protein modeling suite Rosetta, which allows flexible combination with existing protocols to model the modified sites and understand their impact on protein stability as well as function. Lastly, we developed a new design protocol that either maximizes or minimizes the predicted probability of a particular site being modified. We find that this combination of ANN prediction and structure-based design can enable the modification of existing, as well as the introduction of novel, PTMs. The potential applications of our work include, but are not limited to, glycan masking of epitopes, strengthening protein-protein interactions through phosphorylation, as well as protecting proteins from deamidation liabilities. These applications are especially important for the design of new protein therapeutics where PTMs can drastically change the therapeutic properties of a protein. Our work adds novel tools to Rosetta's protein engineering toolbox that allow for the rational design of PTMs. Author summary: Machine learning (ML) is changing the world of protein design, from structure prediction methods like AlphaFold to fixed-backbone design methods like ProteinMPNN. ML methods have made much progress in various aspects of protein computational biology, both complementing and, in some cases, surpassing traditional macromolecular modeling methods such as those combined in libraries like the Rosetta software suite. However, a lack of compatibility and flexibility can hinder interoperability with existing methods, preventing the full potential of these new solutions from being realized. Here, we first present a new machine learning tool for predicting post-translational modifications (PTMs), which play an important role in the stability and function of proteins, and then highlight how the implementation of this tool in the existing Rosetta toolbox can facilitate new applications. To this end, we combine PTM prediction with protein design, maximizing or minimizing the predicted probability of a post-translational modification occurring at a specific site. As one example, we predict the N-linked glycosylation of influenza hemagglutinin, which has applications in both understanding the evolution of viral strains over time, and engineering additional glycosylation sites to mask unwanted epitopes of vaccine candidates. [ABSTRACT FROM AUTHOR]

Discovering mathematical equations that govern physical and biological systems from observed data is a fundamental challenge in scientific research. We present a new physics-informed framework for parameter estimation and missing physics identification (gray-box) in the field of Systems Biology. The proposed framework—named AI-Aristotle—combines the eXtreme Theory of Functional Connections (X-TFC) domain-decomposition and Physics-Informed Neural Networks (PINNs) with symbolic regression (SR) techniques for parameter discovery and gray-box identification. We test the accuracy, speed, flexibility, and robustness of AI-Aristotle based on two benchmark problems in Systems Biology: a pharmacokinetics drug absorption model and an ultradian endocrine model for glucose-insulin interactions. We compare the two machine learning methods (X-TFC and PINNs), and moreover, we employ two different symbolic regression techniques to cross-verify our results. To test the performance of AI-Aristotle, we use sparse synthetic data perturbed by uniformly distributed noise. More broadly, our work provides insights into the accuracy, cost, scalability, and robustness of integrating neural networks with symbolic regressors, offering a comprehensive guide for researchers tackling gray-box identification challenges in complex dynamical systems in biomedicine and beyond. Author summary: Our study addresses the fundamental challenge of uncovering mathematical rules governing physical and biological systems from real-world data. We introduce a novel framework, AI-Aristotle, designed for parameter estimation and identifying hidden physics (gray-box) in Systems Biology. AI-Aristotle combines the powerful eXtreme Theory of Functional Connections (X-TFC), Physics-Informed Neural Networks (PINNs), and symbolic regression (SR) techniques to discover parameters and uncover hidden relationships. Our work offers guidance to researchers addressing gray-box identification challenges in complex dynamic systems, including applications in biomedicine and beyond. [ABSTRACT FROM AUTHOR]

Synthetic biology dictates the data-driven engineering of biocatalysis, cellular functions, and organism behavior. Integral to synthetic biology is the aspiration to efficiently find, access, interoperate, and reuse high-quality data on genotype-phenotype relationships of native and engineered biosystems under FAIR principles, and from this facilitate forward-engineering strategies. However, biology is complex at the regulatory level, and noisy at the operational level, thus necessitating systematic and diligent data handling at all levels of the design, build, and test phases in order to maximize learning in the iterative design-build-test-learn engineering cycle. To enable user-friendly simulation, organization, and guidance for the engineering of biosystems, we have developed an open-source python-based computer-aided design and analysis platform operating under a literate programming user-interface hosted on Github. The platform is called teemi and is fully compliant with FAIR principles. In this study we apply teemi for i) designing and simulating bioengineering, ii) integrating and analyzing multivariate datasets, and iii) machine-learning for predictive engineering of metabolic pathway designs for production of a key precursor to medicinal alkaloids in yeast. The teemi platform is publicly available at PyPi and GitHub. Author summary: Bioengineering holds fantastic perspectives and is poised to change how we produce foods, materials, and medicines. However, rapid progress is limited by a lack of mechanistic knowledge in even the simplest model organisms, such as bacteria and yeast. Thus, to compensate, we often have to construct and study a large number of engineered cells and select the cells with the greatest potential for the given objective function. The targeted construction of engineered cells is often described as an iterative process of design, build, test, and learn (the DBTL cycle). Literate programming is a paradigm that encourages the combination of text and computer code with the potential to describe all workflows covered by the DBTL cycle. The purpose of the present work is to give a first estimate of the extent to which we can accelerate the individual steps in the DBTL cycle by using end-to-end literate programming workflows. To achieve this, we established an open-source platform called teemi, and used it to optimize production of a key precursor to medicinal alkaloids in yeast. We expect that teemi will enable higher DBTL throughput with fewer errors, better integration of IT tools with laboratory resources, and more effective knowledge capture. [ABSTRACT FROM AUTHOR]

The article discusses the use of large language models (LLMs), such as OpenAI's ChatGPT and Google Bard, in the grant writing process. LLM-driven chatbots have become popular tools for drafting and revising scientific text, but there are concerns about ethical issues, privacy, and the potential for generating false information. The article provides ten rules for using LLMs in grant writing, including checking the guidelines of funding agencies, considering data privacy limitations, not relying solely on AI to write the grant, fact-checking everything, using AI-generated text as inspiration rather than copying it directly, and seeking feedback from human reviewers. The authors emphasize the importance of maintaining control over the text and continuously improving grant writing skills. They also highlight the potential benefits of AI in democratizing the grant writing process and helping non-native English writers overcome language barriers. [Extracted from the article]

Learning from past experience is an important adaptation and theoretical models may help to understand its evolution. Many of the existing models study simple phenotypes and do not consider the mechanisms underlying learning while the more complex neural network models often make biologically unrealistic assumptions and rarely consider evolutionary questions. Here, we present a novel way of modelling learning using small neural networks and a simple, biology-inspired learning algorithm. Learning affects only part of the network, and it is governed by the difference between expectations and reality. We use this model to study the evolution of learning under various environmental conditions and different scenarios for the trade-off between exploration (learning) and exploitation (foraging). Efficient learning readily evolves in our individual-based simulations. However, in line with previous studies, the evolution of learning is less likely in relatively constant environments, where genetic adaptation alone can lead to efficient foraging, or in short-lived organisms that cannot afford to spend much of their lifetime on exploration. Once learning does evolve, the characteristics of the learning strategy (i.e. the duration of the learning period and the learning rate) and the average performance after learning are surprisingly little affected by the frequency and/or magnitude of environmental change. In contrast, an organism's lifespan and the distribution of resources in the environment have a strong effect on the evolved learning strategy: a shorter lifespan and a narrow resource distribution reduce the likelihood of learning to evolve. Interestingly, a longer learning period does not always lead to better performance, indicating that the evolved neural networks differ in the effectiveness of learning. Overall, however, we show that a biologically inspired, yet relatively simple, learning mechanism can evolve to lead to an efficient adaptation in a changing environment. Author summary: The ability to learn from experience is an important adaptation. However, it is still unclear how learning is shaped by natural selection. Here, we present a novel way of modelling the evolution of learning using small neural networks and a simple, biology-inspired learning mechanism. Computer simulations reveal that efficient learning readily evolves in this model. However, the evolution of learning is less likely in relatively constant environments (where evolved inborn preferences can guide animal behaviour) and in short-lived organisms (that cannot afford to spend much of their lifetime on learning). If learning does evolve, the evolved learning strategy is strongly affected by the lifespan and environmental richness but surprisingly little by the rate and degree of environmental change. In summary, we show that a simple and biologically plausible mechanism can help understand the evolution of learning and the structure of the evolved learning strategies. [ABSTRACT FROM AUTHOR]

Transformers have revolutionized machine learning models of language and vision, but their connection with neuroscience remains tenuous. Built from attention layers, they require a mass comparison of queries and keys that is difficult to perform using traditional neural circuits. Here, we show that neurons can implement attention-like computations using short-term, Hebbian synaptic potentiation. We call our mechanism the match-and-control principle and it proposes that when activity in an axon is synchronous, or matched, with the somatic activity of a neuron that it synapses onto, the synapse can be briefly strongly potentiated, allowing the axon to take over, or control, the activity of the downstream neuron for a short time. In our scheme, the keys and queries are represented as spike trains and comparisons between the two are performed in individual spines allowing for hundreds of key comparisons per query and roughly as many keys and queries as there are neurons in the network. Author summary: Many of the most impressive recent advances in machine learning, from generating images from text to human-like chatbots, are based on a neural network architecture known as the transformer. Transformers are built from so-called attention layers which perform large numbers of comparisons between the vector outputs of the previous layers, allowing information to flow through the network in a more dynamic way than previous designs. This large number of comparisons is computationally expensive and has no known analogue in the brain. Here, we show that a variation on a learning mechanism familiar in neuroscience, Hebbian learning, can implement a transformer-like attention computation if the synaptic weight changes are large and rapidly induced. We call our method the match-and-control principle and it proposes that when presynaptic and postsynaptic spike trains match up, small groups of synapses can be transiently potentiated allowing a few presynaptic axons to control the activity of a neuron. To demonstrate the principle, we build a model of a pyramidal neuron and use it to illustrate the power and limitations of the idea. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been characterised by sequential variant-specific waves shaped by viral, individual human and population factors. SARS-CoV-2 variants are defined by their unique combinations of mutations and there has been a clear adaptation to more efficient human infection since the emergence of this new human coronavirus in late 2019. Here, we use machine learning models to identify shared signatures, i.e., common underlying mutational processes and link these to the subset of mutations that define the variants of concern (VOCs). First, we examined the global SARS-CoV-2 genomes and associated metadata to determine how viral properties and public health measures have influenced the magnitude of waves, as measured by the number of infection cases, in different geographic locations using regression models. This analysis showed that, as expected, both public health measures and virus properties were associated with the waves of regional SARS-CoV-2 reported infection numbers and this impact varies geographically. We attribute this to intrinsic differences such as vaccine coverage, testing and sequencing capacity and the effectiveness of government stringency. To assess underlying evolutionary change, we used non-negative matrix factorisation and observed three distinct mutational signatures, unique in their substitution patterns and exposures from the SARS-CoV-2 genomes. Signatures 1, 2 and 3 were biased to C→T, T→C/A→G and G→T point mutations. We hypothesise assignments of these mutational signatures to the host antiviral molecules APOBEC, ADAR and ROS respectively. We observe a shift amidst the pandemic in relative mutational signature activity from predominantly Signature 1 changes to an increasingly high proportion of changes consistent with Signature 2. This could represent changes in how the virus and the host immune response interact and indicates how SARS-CoV-2 may continue to generate variation in the future. Linkage of the detected mutational signatures to the VOC-defining amino acids substitutions indicates the majority of SARS-CoV-2's evolutionary capacity is likely to be associated with the action of host antiviral molecules rather than virus replication errors. Author summary: We show that both public health measures and virus properties are associated with the rise and fall of regional SARS-CoV-2 reported infection numbers with regional differences attributable to the extent of vaccine usage and the effectiveness of public health measures. In our mutational signature analysis, using non-negative matrix factorisation, we detected three distinct mutational signatures that can be putatively attributed to the action of specific host antiviral molecules. Interestingly, we observe a shift in mutational signature activity from predominantly Signature 1 changes to an increasingly high proportion of changes consistent with Signature 2. These mutation patterns influence SARS-CoV-2's evolutionary capacity, the available genetic variation that selection can act on, and so can be linked to the mutations defining the variants of concern responsible for the distinct SARS-CoV-2 infection waves. The dominant types of nucleotide substitutions involved indicate that much of the mutation and hence variation come from the action of the host immune response rather than replication errors since the virus has an error correction system. [ABSTRACT FROM AUTHOR]

The transcriptional regulatory network (TRN) of E. coli consists of thousands of interactions between regulators and DNA sequences. Regulons are typically determined either from resource-intensive experimental measurement of functional binding sites, or inferred from analysis of high-throughput gene expression datasets. Recently, independent component analysis (ICA) of RNA-seq compendia has shown to be a powerful method for inferring bacterial regulons. However, it remains unclear to what extent regulons predicted by ICA structure have a biochemical basis in promoter sequences. Here, we address this question by developing machine learning models that predict inferred regulon structures in E. coli based on promoter sequence features. Models were constructed successfully (cross-validation AUROC > = 0.8) for 85% (40/47) of ICA-inferred E. coli regulons. We found that: 1) The presence of a high scoring regulator motif in the promoter region was sufficient to specify regulatory activity in 40% (19/47) of the regulons, 2) Additional features, such as DNA shape and extended motifs that can account for regulator multimeric binding, helped to specify regulon structure for the remaining 60% of regulons (28/47); 3) investigating regulons where initial machine learning models failed revealed new regulator-specific sequence features that improved model accuracy. Finally, we found that strong regulatory binding sequences underlie both the genes shared between ICA-inferred and experimental regulons as well as genes in the E. coli core pan-regulon of Fur. This work demonstrates that the structure of ICA-inferred regulons largely can be understood through the strength of regulator binding sites in promoter regions, reinforcing the utility of top-down inference for regulon discovery. Author summary: The Transcriptional Regulatory Network (TRN) of bacteria is a gene expression control system largely governed by transcription factors binding to promoter regions of the genome, forming a connected network of regulons. While experimental determination of functional transcription factor binding sites is laborious, inference of regulons from large-scale gene expression datasets has proven to be a powerful alternative. Here, we seek to determine whether inferred regulons have a biochemical basis in terms of binding sites sequences present at gene promoters. We use machine learning to build quantitative models relating binding site features to gene regulation. We find that the majority of data-inferred regulons can be tied to binding site sequences at gene promoters, supporting the biological reality of inferred regulons. Additionally, these inferred regulons sometimes carried additional sequence context that is not accounted for by the transcription factor binding motif extracted from experimental binding sites. An understanding of how regulons are encoded in the genome will empower sequence analysis and synthetic biology applications. [ABSTRACT FROM AUTHOR]

Network-based machine learning (ML) has the potential for predicting novel genes associated with nearly any health and disease context. However, this approach often uses network information from only the single species under consideration even though networks for most species are noisy and incomplete. While some recent methods have begun addressing this shortcoming by using networks from more than one species, they lack one or more key desirable properties: handling networks from more than two species simultaneously, incorporating many-to-many orthology information, or generating a network representation that is reusable across different types of and newly-defined prediction tasks. Here, we present GenePlexusZoo, a framework that casts molecular networks from multiple species into a single reusable feature space for network-based ML. We demonstrate that this multi-species network representation improves both gene classification within a single species and knowledge-transfer across species, even in cases where the inter-species correspondence is undetectable based on shared orthologous genes. Thus, GenePlexusZoo enables effectively leveraging the high evolutionary molecular, functional, and phenotypic conservation across species to discover novel genes associated with diverse biological contexts. Author summary: Our work addresses two major challenges; 1) computationally predicting the role a gene plays in various diseases, processes and phenotypes, and 2) accurately transferring genetic information discovered in one species to another. To simultaneously tackle both of these challenges, we developed the GenePlexusZoo method which builds a gene classification model by utilizing molecular interaction information from multiple species, seamlessly handling the complicated mapping of how genes across species are functionally related. We show that machine learning classifiers that utilize information from multiple species outperform those that only consider information from a single species. Additionally, we show that the GenePelxusZoo method is able to accurately transfer knowledge from one species to another, even in the cases where no previous connection would have been detected based solely on shared orthologous genes. Finally, we present an illustrative example of how GenePlexusZoo can provide novel insights into a complicated genetic-based disease. [ABSTRACT FROM AUTHOR]