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In this paper, a molecular imaging method employing acetic acid dilute solution as a biomarker is described. An interpretation of the biophysical processes that are involved in the biomarker-tissue interaction and are determining the in vivo measured dynamic scattering characteristics is presented. On the basis of this interpretation, a compartmental model of the epithelium is developed for predicting the epithelial transport phenomena that are expected to be correlated with the dynamic characteristics of the backscattered light. The model predictions have been compared with the experimental data obtained from patients with cervical neoplasia of different grade, with the aid of a specially developed imaging system. Comparisons confirmed the validity of the interpretation of the phenomenon, and particularly, the fact that dynamic scattering characteristics are largely determined by the intracellular proton concentration kinetics. In addition, the correlation of the latter with both structural and functional alterations, associated with cervical neoplasia development, has been predicted theoretically and confirmed experimentally. The established correlation enables the derivation of quantitative indices expressing disease-specific microstructural and functional alterations, from the in vivo measured dynamic optical characteristics. This highlights the potential of the developed imaging method and technology for the noninvasive diagnosis, guided therapeutics, and screening of cervical neoplasia. [ABSTRACT FROM PUBLISHER]

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices., Competing Interests: CJ is employed by Biome diagnostics GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2023 D’Elia, Truu, Lahti, Berland, Papoutsoglou, Ceci, Zomer, Lopes, Ibrahimi, Gruca, Nechyporenko, Frohme, Klammsteiner, Pau, Marcos-Zambrano, Hron, Pio, Simeon, Suharoschi, Moreno-Indias, Temko, Nedyalkova, Apostol, Truică, Shigdel, Telalović, Bongcam-Rudloff, Przymus, Jordamović, Falquet, Tarazona, Sampri, Isola, Pérez-Serrano, Trajkovik, Klucar, Loncar-Turukalo, Havulinna, Jansen, Bertelsen and Claesson.)

Microbiome data predictive analysis within a machine learning (ML) workflow presents numerous domain-specific challenges involving preprocessing, feature selection, predictive modeling, performance estimation, model interpretation, and the extraction of biological information from the results. To assist decision-making, we offer a set of recommendations on algorithm selection, pipeline creation and evaluation, stemming from the COST Action ML4Microbiome. We compared the suggested approaches on a multi-cohort shotgun metagenomics dataset of colorectal cancer patients, focusing on their performance in disease diagnosis and biomarker discovery. It is demonstrated that the use of compositional transformations and filtering methods as part of data preprocessing does not always improve the predictive performance of a model. In contrast, the multivariate feature selection, such as the Statistically Equivalent Signatures algorithm, was effective in reducing the classification error. When validated on a separate test dataset, this algorithm in combination with random forest modeling, provided the most accurate performance estimates. Lastly, we showed how linear modeling by logistic regression coupled with visualization techniques such as Individual Conditional Expectation (ICE) plots can yield interpretable results and offer biological insights. These findings are significant for clinicians and non-experts alike in translational applications., Competing Interests: GP was directly affiliated with JADBio—Gnosis DA, S.A., which offers the JADBio service commercially. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Papoutsoglou, Tarazona, Lopes, Klammsteiner, Ibrahimi, Eckenberger, Novielli, Tonda, Simeon, Shigdel, Béreux, Vitali, Tangaro, Lahti, Temko, Claesson and Berland.)

Since the onset of the COVID-19 pandemic, increasing cases with variable outcomes continue globally because of variants and despite vaccines and therapies. There is a need to identify at-risk individuals early that would benefit from timely medical interventions. DNA methylation provides an opportunity to identify an epigenetic signature of individuals at increased risk. We utilized machine learning to identify DNA methylation signatures of COVID-19 disease from data available through NCBI Gene Expression Omnibus. A training cohort of 460 individuals (164 COVID-19-infected and 296 non-infected) and an external validation dataset of 128 individuals (102 COVID-19-infected and 26 non-COVID-associated pneumonia) were reanalyzed. Data was processed using ChAMP and beta values were logit transformed. The JADBio AutoML platform was leveraged to identify a methylation signature associated with severe COVID-19 disease. We identified a random forest classification model from 4 unique methylation sites with the power to discern individuals with severe COVID-19 disease. The average area under the curve of receiver operator characteristic (AUC-ROC) of the model was 0.933 and the average area under the precision-recall curve (AUC-PRC) was 0.965. When applied to our external validation, this model produced an AUC-ROC of 0.898 and an AUC-PRC of 0.864. These results further our understanding of the utility of DNA methylation in COVID-19 disease pathology and serve as a platform to inform future COVID-19 related studies., (© 2022. The Author(s).)

Fully automated machine learning (AutoML) for predictive modeling is becoming a reality, giving rise to a whole new field. We present the basic ideas and principles of Just Add Data Bio (JADBio), an AutoML platform applicable to the low-sample, high-dimensional omics data that arise in translational medicine and bioinformatics applications. In addition to predictive and diagnostic models ready for clinical use, JADBio focuses on knowledge discovery by performing feature selection and identifying the corresponding biosignatures, i.e., minimal-size subsets of biomarkers that are jointly predictive of the outcome or phenotype of interest. It also returns a palette of useful information for interpretation, clinical use of the models, and decision making. JADBio is qualitatively and quantitatively compared against Hyper-Parameter Optimization Machine Learning libraries. Results show that in typical omics dataset analysis, JADBio manages to identify signatures comprising of just a handful of features while maintaining competitive predictive performance and accurate out-of-sample performance estimation., (© 2022. The Author(s).)

COVID-19 outbreak brings intense pressure on healthcare systems, with an urgent demand for effective diagnostic, prognostic and therapeutic procedures. Here, we employed Automated Machine Learning (AutoML) to analyze three publicly available high throughput COVID-19 datasets, including proteomic, metabolomic and transcriptomic measurements. Pathway analysis of the selected features was also performed. Analysis of a combined proteomic and metabolomic dataset led to 10 equivalent signatures of two features each, with AUC 0.840 (CI 0.723-0.941) in discriminating severe from non-severe COVID-19 patients. A transcriptomic dataset led to two equivalent signatures of eight features each, with AUC 0.914 (CI 0.865-0.955) in identifying COVID-19 patients from those with a different acute respiratory illness. Another transcriptomic dataset led to two equivalent signatures of nine features each, with AUC 0.967 (CI 0.899-0.996) in identifying COVID-19 patients from virus-free individuals. Signature predictive performance remained high upon validation. Multiple new features emerged and pathway analysis revealed biological relevance by implication in Viral mRNA Translation, Interferon gamma signaling and Innate Immune System pathways. In conclusion, AutoML analysis led to multiple biosignatures of high predictive performance, with reduced features and large choice of alternative predictors. These favorable characteristics are eminent for development of cost-effective assays to contribute to better disease management., (© 2021. The Author(s).)

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Marcos-Zambrano, Karaduzovic-Hadziabdic, Loncar Turukalo, Przymus, Trajkovik, Aasmets, Berland, Gruca, Hasic, Hron, Klammsteiner, Kolev, Lahti, Lopes, Moreno, Naskinova, Org, Paciência, Papoutsoglou, Shigdel, Stres, Vilne, Yousef, Zdravevski, Tsamardinos, Carrillo de Santa Pau, Claesson, Moreno-Indias and Truu.)

Single-cell platforms provide statistically large samples of snapshot observations capable of resolving intrercellular heterogeneity. Currently, there is a growing literature on algorithms that exploit this attribute in order to infer the trajectory of biological mechanisms, such as cell proliferation and differentiation. Despite the efforts, the trajectory inference methodology has not yet been used for addressing the challenging problem of learning the dynamics of protein signaling systems. In this work, we assess this prospect by testing the performance of this class of algorithms on four proteomic temporal datasets. To evaluate the learning quality, we design new general-purpose evaluation metrics that are able to quantify performance on (i) the biological meaning of the output, (ii) the consistency of the inferred trajectory, (iii) the algorithm robustness, (iv) the correlation of the learning output with the initial dataset, and (v) the roughness of the cell parameter levels though the inferred trajectory. We show that experimental time alone is insufficient to provide knowledge about the order of proteins during signal transduction. Accordingly, we show that the inferred trajectories provide richer information about the underlying dynamics. We learn that established methods tested on high-dimensional data with small sample size, slow dynamics, and complex structures (e.g. bifurcations) cannot always work in the signaling setting. Among the methods we evaluate, Scorpius and a newly introduced approach that combines Diffusion Maps and Principal Curves were found to perform adequately in recovering the progression of signal transduction although their performance on some metrics varies from one dataset to another. The novel metrics we devise highlight that it is difficult to conclude, which one method is universally applicable for the task. Arguably, there are still many challenges and open problems to resolve. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry., (© 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.)

Cytometry by time-of-flight (CyTOF) has emerged as a high-throughput single cell technology able to provide large samples of protein readouts. Already, there exists a large pool of advanced high-dimensional analysis algorithms that explore the observed heterogeneous distributions making intriguing biological inferences. A fact largely overlooked by these methods, however, is the effect of the established data preprocessing pipeline to the distributions of the measured quantities. In this article, we focus on randomization, a transformation used for improving data visualization, which can negatively affect multivariate data analysis methods such as dimensionality reduction, clustering, and network reconstruction algorithms. Our results indicate that randomization should be used only for visualization purposes, but not in conjunction with high-dimensional analytical tools. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry., (© 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.)

The differentiation of self-renewing progenitor cells requires not only the regulation of lineage- and developmental stage-specific genes but also the coordinated adaptation of housekeeping functions from a metabolically active, proliferative state toward quiescence. How metabolic and cell-cycle states are coordinated with the regulation of cell type-specific genes is an important question, because dissociation between differentiation, cell cycle, and metabolic states is a hallmark of cancer. Here, we use a model system to systematically identify key transcriptional regulators of Ikaros-dependent B cell-progenitor differentiation. We find that the coordinated regulation of housekeeping functions and tissue-specific gene expression requires a feedforward circuit whereby Ikaros down-regulates the expression of Myc. Our findings show how coordination between differentiation and housekeeping states can be achieved by interconnected regulators. Similar principles likely coordinate differentiation and housekeeping functions during progenitor cell differentiation in other cell lineages., Competing Interests: The authors have declared that no competing interests exist.

Background: Reptiles are a species-rich group with great phenotypic and life history diversity but are highly underrepresented among the vertebrate species with sequenced genomes., Results: Here, we report a high-quality genome assembly of the tegu lizard, Salvator merianae, the first lacertoid with a sequenced genome. We combined 74X Illumina short-read, 29.8X Pacific Biosciences long-read, and optical mapping data to generate a high-quality assembly with a scaffold N50 value of 55.4 Mb. The contig N50 value of this assembly is 521 Kb, making it the most contiguous reptile assembly so far. We show that the tegu assembly has the highest completeness of coding genes and conserved non-exonic elements (CNEs) compared to other reptiles. Furthermore, the tegu assembly has the highest number of evolutionarily conserved CNE pairs, corroborating a high assembly contiguity in intergenic regions. As in other reptiles, long interspersed nuclear elements comprise the most abundant transposon class. We used transcriptomic data, homology- and de novo gene predictions to annotate 22,413 coding genes, of which 16,995 (76%) likely have human orthologs as inferred by CESAR-derived gene mappings. Finally, we generated a multiple genome alignment comprising 10 squamates and 7 other amniote species and identified conserved regions that are under evolutionary constraint. CNEs cover 38 Mb (1.8%) of the tegu genome, with 3.3 Mb in these elements being squamate specific. In contrast to placental mammal-specific CNEs, very few of these squamate-specific CNEs (<20 Kb) overlap transposons, highlighting a difference in how lineage-specific CNEs originated in these two clades., Conclusions: The tegu lizard genome together with the multiple genome alignment and comprehensive conserved element datasets provide a valuable resource for comparative genomic studies of reptiles and other amniotes.

Flow and mass cytometry technologies can probe proteins as biological markers in thousands of individual cells simultaneously, providing unprecedented opportunities for reconstructing networks of protein interactions through machine learning algorithms. The network reconstruction (NR) problem has been well-studied by the machine learning community. However, the potentials of available methods remain largely unknown to the cytometry community, mainly due to their intrinsic complexity and the lack of comprehensive, powerful and easy-to-use NR software implementations specific for cytometry data. To bridge this gap, we present Single CEll NEtwork Reconstruction sYstem (SCENERY), a web server featuring several standard and advanced cytometry data analysis methods coupled with NR algorithms in a user-friendly, on-line environment. In SCENERY, users may upload their data and set their own study design. The server offers several data analysis options categorized into three classes of methods: data (pre)processing, statistical analysis and NR. The server also provides interactive visualization and download of results as ready-to-publish images or multimedia reports. Its core is modular and based on the widely-used and robust R platform allowing power users to extend its functionalities by submitting their own NR methods. SCENERY is available at scenery.csd.uoc.gr or http://mensxmachina.org/en/software/., (© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.)

We present a novel biophotonic method and imaging modality for estimating and mapping neoplasia-specific functional and structural parameters of the cervical precancerous epithelium. Estimations were based on experimental data obtained from dynamic contrast-enhanced optical imaging of cervix, in vivo. We have developed a pharmacokinetic, in silico, model of the optical tracer's uptake by the epithelium. We have identified that the kinetic parameters of the model correlate well with pathologic alterations in both metabolic and structural characteristics of the tissue, associated with the neoplasia progress. Global sensitivity analysis and global optimization methods were employed for identifying the key determinant set of biological parameters that dictate the model's output. Particularly, the shuffled complex evolution algorithm converged to a set of four parameters that can be estimated with an error of 7%, indicating a good accuracy and precision. These results are unique in the sense that for the first time functional and microstructural parameter maps can be estimated and displayed together, thus maximizing the diagnostic information. The quantity and the quality of this information are unattainable by other invasive and non invasive methods.

We present a method for detecting and studying neoplasia-specific functional and structural features through the combination of in vivo dynamic imaging, in silico modeling and global sensitivity analysis. We particularly present the case of cervical epithelium interacting with acetic acid solution, which is employed as an optical biomarker. The in vivo measured dynamic scattering characteristics are strongly correlated with the output of the biomarker's pharmacokinetic model that we have developed. Model global sensitivity analysis has shown that the measured/modeled bio-optical processes can be used for probing, in vivo, the number of neoplastic layers, the extracellular pH, the intracellular buffering efficiency and the size of the extracellular space.

Voltage-dependent anion channels (VDACs) are pore-forming proteins (porins) that form the major pathway for movement of adenine nucleotides through the outer mitochondrial membrane. Electrophysiological studies indicate that VDAC-like channel activity is also prevalent in the cell membranes of many mammalian cells. However, the multitopological localization of porins outside the mitochondrion has remained an extremely controversial issue. Herein, we show that usage of two alternative first exons of the murine VDAC-1 gene leads to expression of two porins differing within their N termini. One porin (plasmalemmal VDAC-1) harboring a hydrophobic leader peptide is primarily targeted through the Golgi apparatus to the cell membrane. In contrast, the second isoform lacking the N-terminal leader (mitochondrial VDAC-1) is translocated more efficiently into the outer mitochondrial membrane. Thus, our data provide unique genetic evidence in favor of a multitopological localization of a mitochondrial porin.