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AbstractWith the rapid development of quantum computers, researchers have shown quantum advantages in physics-oriented problems. Quantum algorithms tackling computational biology problems are still lacking. In this paper, we demonstrate the quantum advantage in analyzing CITE-seq data. CITE-seq, a single-cell technology, enables researchers to simultaneously measure expressions of RNA and surface protein detected by antibody-derived tags (ADTs) in the same cells. CITE-seq data hold tremendous potential for identifying the ADTs associated with targeted genes and identifying cell types effectively. However, both tasks are challenging since the best subset of ADTs needs to be identified from enormous candidate subsets. To surmount the challenge, we develop a quantum algorithm named bisection Grover’s search (BGS) for the best subset selection of ADT markers in CITE-seq data. BGS takes advantage of quantum parallelism by integrating binary search and Grover’s algorithm to enable fast computation. Theoretical results are provided to show the privilege of BGS in the estimation error and computational complexity. The empirical performance of the BGS algorithm is demonstrated on both the IBM quantum computer and simulator. [ABSTRACT FROM AUTHOR]

Western blot is a popular biomolecular analysis method for measuring the relative quantities of independent proteins in complex biological samples. However, variability in quantitative western blot data analysis poses a challenge in designing reproducible experiments. The lack of rigorous quantitative approaches in current western blot statistical methodology may result in irreproducible inferences. Here we describe best practices for the design and analysis of western blot experiments, with examples and demonstrations of how different analytical approaches can lead to widely varying outcomes. To facilitate best practices, we have developed the blotRig tool for designing and analyzing western blot experiments to improve their rigor and reproducibility. The blotRig application includes functions for counterbalancing experimental design by lane position, batch management across gels, and analytics with covariates and random effects. [ABSTRACT FROM AUTHOR]

Inferring gene regulatory networks through deep learning and causal inference methods is a crucial task in the field of computational biology and bioinformatics. This study presents a novel approach that uses a Graph Convolutional Network (GCN) guided by causal information to infer Gene Regulatory Networks (GRN). The transfer entropy and reconstruction layer are utilized to achieve causal feature reconstruction, mitigating the information loss problem caused by multiple rounds of neighbor aggregation in GCN, resulting in a causal and integrated representation of node features. Separable features are extracted from gene expression data by the Gaussian-kernel Autoencoder to improve computational efficiency. Experimental results on the DREAM5 and the mDC dataset demonstrate that our method exhibits superior performance compared to existing algorithms, as indicated by the higher values of the AUPRC metrics. Furthermore, the incorporation of causal feature reconstruction enhances the inferred GRN, rendering them more reasonable, accurate, and reliable. [ABSTRACT FROM AUTHOR]

As genomics technologies advance, there is a growing demand for computational biologists trained for genomics analysis but instructors face significant hurdles in providing formal training in computer programming, statistics, and genomics to biology students. Fully online learners represent a significant and growing community that can contribute to meet this need, but they are frequently excluded from valuable research opportunities which mostly do not offer the flexibility they need. To address these opportunity gaps, we developed an asynchronous course-based undergraduate research experience (CURE) for computational genomics specifically for fully online biology students. We generated custom learning materials and leveraged remotely accessible computational tools to address 2 novel research questions over 2 iterations of the genomics CURE, one testing bioinformatics approaches and one mining cancer genomics data. Here, we present how the instructional team distributed analysis needed to address these questions between students over a 7.5-week CURE and provided concurrent training in biology and statistics, computer programming, and professional development. Scores from identical learning assessments administered before and after completion of each CURE showed significant learning gains across biology and coding course objectives. Open-response progress reports were submitted weekly and identified self-reported adaptive coping strategies for challenges encountered throughout the course. Progress reports identified problems that could be resolved through collaboration with instructors and peers via messaging platforms and virtual meetings. We implemented asynchronous communication using the Slack messaging platform and an asynchronous journal club where students discussed relevant publications using the Perusall social annotation platform. The online genomics CURE resulted in unanticipated positive outcomes, including students voluntarily discussing plans to continue research after the course. These outcomes underscore the effectiveness of this genomics CURE for scientific training, recruitment and student-mentor relationships, and student successes. Asynchronous genomics CUREs can contribute to a more skilled, diverse, and inclusive workforce for the advancement of biomedical science. Author summary: As technology advances, there is a growing demand for research scientists trained in computational biology but it can be difficult to introduce computer programming and statistics to biology students. One way to meet this demand in an inclusive way is to provide more research opportunities for online students, a significant and growing community which includes many groups underrepresented in the science workforce. We present a course designed for fully online undergraduate biology students where they can work asynchronously to address a novel research question. We show how we divided research projects among the students of the class, leveraged remotely accessible computational tools and online messaging platforms, and created custom learning materials and assessments to teach the students the necessary biology, computer programming, and communication skills needed for each research project. We demonstrate that students were able to learn the course objectives and cope with academic stresses. Research can be designed around questions in many topics, so we hope that our design can help others to create remote computational research courses in their field. [ABSTRACT FROM AUTHOR]

In 2024, all biology is computational biology. Computer-aided analysis continues to spread into new fields, becoming more accessible to researchers trained in the wet lab who are eager to take advantage of growing datasets, falling costs, and novel assays that present new opportunities for discovery. It is currently much easier to find guidance for implementing these techniques than for reporting their use, leaving biologists to guess which details and files are relevant. In this essay, we review existing literature on the topic, summarize common tips, and link to additional resources for training. Following this overview, we then provide a set of recommendations for sharing code, with an eye toward guiding those who are comparatively new to applying open science principles to their computational work. Taken together, we provide a guide for biologists who seek to follow code sharing best practices but are unsure where to start. For those who want to share their code but don't know where to start, this Essay distils dozens of articles on reproducibility and research software, collecting the most important practical details of how to provide computational transparency even if you aren't a trained software developer. [ABSTRACT FROM AUTHOR]

γδ T-cells are a rare population of T-cells with both adaptive and innate-like properties. Despite their low prevalence, they have been found to be implicated various human diseases. γδ T-cell infiltration has been associated with improved clinical outcomes in solid cancers, prompting renewed interest in understanding their biology. To date, their biology remains elusive due to their low prevalence. The introduction of high-resolution single-cell sequencing has allowed various groups to characterize key effector subsets in various contexts, as well as begin to elucidate key regulatory mechanisms directing the differentiation and activity of these cells. In this review, we will review some of insights obtained from singlecell studies of γδ T-cells across various malignancies and highlight some important questions that remain unaddressed. [ABSTRACT FROM AUTHOR]

Bats are natural reservoirs for zoonotic pathogens, yet the determinants of microbial persistence as well as the specific functionality of their immune system remain largely enigmatic. Their propensity to harbor viruses lethal to humans and/or livestock, mostly in absence of clinical disease, makes bats stand out among mammals. Defending against pathogens relies on avoidance, resistance, and/or tolerance strategies. In bats, disease tolerance has recently gained increasing attention as a prevailing host defense paradigm. We here summarize the current knowledge on immune responses in bats in the context of infection with zoonotic agents and discuss concepts related to disease tolerance. Acknowledging the wide diversity of bats, the broad spectrum of bat-associated microbial species, and immune-related knowledge gaps, we identify research priorities necessary to provide evidence-based proofs for disease tolerance in bats. Since disease tolerance relies on networks of biological processes, we emphasize that investigations beyond the immune system, using novel technologies and computational biology, could jointly advance our knowledge about mechanisms conferring bats reservoir abilities. Although disease tolerance may not be the "one fit all" defense strategy, deciphering disease tolerance in bats could translate into novel therapies and inform prevention of spillover infections to humans and livestock. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) poses a significant health burden globally, with high mortality rates despite various treatment options. Immunotherapy, particularly immune-checkpoint inhibitors (ICIs), has shown promise, but resistance and metastasis remain major challenges. Understanding the intricacies of the tumor microenvironment (TME) is imperative for optimizing HCC management strategies and enhancing patient prognosis. Methods: This study employed a comprehensive approach integrating multi-omics approaches, including single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing (Bulk RNA-seq), and validation in clinical samples using spatial transcriptomics (ST) and multiplex immunohistochemistry (mIHC). The analysis aimed to identify key factors influencing the immunosuppressive microenvironment associated with HCC metastasis and immunotherapy resistance. Results: HMGB2 is significantly upregulated in HCCTrans, a transitional subgroup associated with aggressive metastasis. Furthermore, HMGB2 expression positively correlates with an immunosuppressive microenvironment, particularly evident in exhausted T cells. Notably, HMGB2 expression correlated positively with immunosuppressive markers and poor prognosis in HCC patients across multiple cohorts. ST combined with mIHC validated the spatial expression patterns of HMGB2 within the TME, providing additional evidence of its role in HCC progression and immune evasion. Conclusion: HMGB2 emerges as a critical player of HCC progression, metastasis, and immunosuppression. Its elevated expression correlates with aggressive tumor behavior and poor patient outcomes, suggesting its potential as both a therapeutic target and a prognostic indicator in HCC management. [ABSTRACT FROM AUTHOR]

Summary: The data deluge in biology calls for computational approaches that can integrate multiple datasets of different types to build a holistic view of biological processes or structures of interest. An emerging paradigm in this domain is the unsupervised learning of data embeddings that can be used for downstream clustering and classification tasks. While such approaches for integrating data of similar types are becoming common, there is scarcer work on consolidating different data modalities such as network and image information. Here, we introduce DICE (Data Integration through Contrastive Embedding), a contrastive learning model for multi-modal data integration. We apply this model to study the subcellular organization of proteins by integrating protein–protein interaction data and protein image data measured in HEK293 cells. We demonstrate the advantage of data integration over any single modality and show that our framework outperforms previous integration approaches. Availability : https://github.com/raminass/protein-contrastive Contact : raminass@gmail.com [ABSTRACT FROM AUTHOR]

Pangenomics is a relatively new scientific field which investigates the union of all the genomes of a clade. The word pan means everything in ancient Greek; the term pangenomics originally regarded genomes of bacteria and was later intended to refer to human genomes as well. Modern bioinformatics offers several tools to analyze pangenomics data, paving the way to an emerging field that we can call computational pangenomics. Current computational power available for the bioinformatics community has made computational pangenomic analyses easy to perform, but this higher accessibility to pangenomics analysis also increases the chances to make mistakes and to produce misleading or inflated results, especially by beginners. To handle this problem, we present here a few quick tips for efficient and correct computational pangenomic analyses with a focus on bacterial pangenomics, by describing common mistakes to avoid and experienced best practices to follow in this field. We believe our recommendations can help the readers perform more robust and sound pangenomic analyses and to generate more reliable results. [ABSTRACT FROM AUTHOR]

Simple Summary: This review provides a comprehensive overview of the current knowledge on mutational signatures in colorectal cancer (CRC), highlighting their potential clinical applications. It discusses the challenges and limitations in translating these analyses into clinical practice and proposes strategies to overcome these obstacles. Additionally, it provides insights into future directions and emerging proof-of-concept studies that highlight the translational potential of mutational signature analysis in improving patient care and outcomes in CRC. A multitude of exogenous and endogenous processes have the potential to result in DNA damage. While the repair mechanisms are typically capable of correcting this damage, errors in the repair process can result in mutations. The findings of research conducted in 2012 indicate that mutations do not occur randomly but rather follow specific patterns that can be attributed to known or inferred mutational processes. The process of mutational signature analysis allows for the inference of the predominant mutational process for a given cancer sample, with significant potential for clinical applications. A deeper comprehension of these mutational signatures in CRC could facilitate enhanced prevention strategies, facilitate the comprehension of genotoxic drug activity, predict responses to personalized treatments, and, in the future, inform the development of targeted therapies in the context of precision oncology. The efforts of numerous researchers have led to the identification of several mutational signatures, which can be categorized into different mutational signature references. In CRC, distinct mutational signatures are identified as correlating with mismatch repair deficiency, polymerase mutations, and chemotherapy treatment. In this context, a mutational signature analysis offers considerable potential for enhancing minimal residual disease (MRD) tests in stage II (high-risk) and stage III CRC post-surgery, stratifying CRC based on the impacts of genetic and epigenetic alterations for precision oncology, identifying potential therapeutic vulnerabilities, and evaluating drug efficacy and guiding therapy, as illustrated in a proof-of-concept clinical trial. [ABSTRACT FROM AUTHOR]

Cyclin-dependent kinase 8 (CDK8) is highly expressed in various cancers and common complex human diseases, and an important therapeutic target for drug discovery and development. The CDK8 inhibitors are actively sought after, especially among natural products. We performed a virtual screening using the ZINC library comprising approximately 90,000 natural compounds. We applied Lipinski's rule of five, absorption, distribution, metabolism, excretion, and toxicity properties, and pan-assay interference compounds filter to eliminate promiscuous binders. Subsequently, the filtered compounds underwent molecular docking to predict their binding affinity and interactions with the CDK8 protein. Interaction analysis were carried out to elucidate the interaction mechanism of the screened hits with binding pockets of the CDK8. The ZINC02152165, ZINC04236005, and ZINC02134595 were selected with appreciable specificity and affinity with CDK8. An all-atom molecular dynamic (MD) simulation followed by essential dynamics was performed for 200 ns. Taken together, the results suggest that ZINC02152165, ZINC04236005, and ZINC02134595 can be harnessed as potential leads in therapeutic development. Moreover, the binding of the molecules brings change in protein conformation in a way that blocks the ATP-binding site of the protein, obstructing its kinase activity. These new findings from natural products offer insights into the molecular mechanisms underlying CDK8 inhibition. CDK8 was previously associated with behavioral and neurological diseases such as autism spectrum disorder, and cancers, for example, colorectal, prostate, breast, and acute myeloid leukemia. Hence, we call for further research and experimental validation, and with an eye to inform future clinical drug discovery and development in these therapeutic fields. [ABSTRACT FROM AUTHOR]

Computational modelling is widely used in biological science. Therefore, biology students need to learn computational modelling. However, there is a lack of evidence about how to teach computational modelling in biology and what the effects are on student learning. The purpose of this intervention-control study was to investigate how knowledge in computational modelling is associated with knowledge acquisition in biology. Participants were 118 students (17 to 19 years of age) enrolled in first and second year of Danish High School. The intervention group (n = 81) received teaching in biology and computational modelling while the comparison group (n = 37) received teaching in biology using textbook models. Both groups received two sessions, each of approximately 120 minutes. The study used mixed methods to analyse students' knowledge of biology and computational modelling. Participants in the intervention group showed statistically significant improvements in their biological knowledge and computational modelling knowledge. The study is one of the first to investigate the effect of computational modelling on high school students' learning of biology. The study discusses how biology could be an important subject where students can learn computing concepts central in the endeavour to introduce computing in high school education. [ABSTRACT FROM AUTHOR]

Robust segmentation of large and complex conjoined tree structures in 3-D is a major challenge in computer vision. This is particularly true in computational biology, where we often encounter large data structures in size, but few in number, which poses a hard problem for learning algorithms. We show that merging multiscale opening with geodesic path propagation, can shed new light on this classic machine vision challenge, while circumventing the learning issue by developing an unsupervised visual geometry approach (digital topology/morphometry). The novelty of the proposed MSO-GP method comes from the geodesic path propagation being guided by a skeletonization of the conjoined structure that helps to achieve robust segmentation results in a particularly challenging task in this area, that of artery-vein separation from non-contrast pulmonary computed tomography angiograms. This is an important first step in measuring vascular geometry to then diagnose pulmonary diseases and to develop image-based phenotypes. We first present proof-of-concept results on synthetic data, and then verify the performance on pig lung and human lung data with less segmentation time and user intervention needs than those of the competing methods. • We revisit the classic computer vision problem of segmenting complex conjoined tree-like structures. • We the branches as skeleton guided geodesic paths and open them morphologically in multiple scales. • The proposed MSO-GP method is unsupervised and non-learning based, hence fast and annotation independent. • We demonstrate the method on artery-vein segmentation in non contrast lung computed tomography (CT) angiograms. • MSO-GP outperforms two competing methods on synthetic generated data, pig lung phantom data and human lung data. [ABSTRACT FROM AUTHOR]

Zika virus (ZIKV), a mosquito‐borne Flavivirus of international concern, causes congenital microcephaly in newborns and Guillain–Barré syndrome in adults. ZIKV capsid (C) protein, one of three key structural proteins, is essential for viral assembly and encapsidation. In dengue virus, a closely related flavivirus, the homologous C protein interacts with host lipid systems, namely intracellular lipid droplets, for successful viral replication. Here, we investigate ZIKV C interaction with host lipid systems, showing that it binds host lipid droplets but, contrary to expected, in an unspecific manner. Contrasting with other flaviviruses, ZIKV C also does not bind very‐low density‐lipoproteins. Comparing with other Flavivirus, capsid proteins show that ZIKV C structure is particularly thermostable and seems to be locked into an auto‐inhibitory conformation due to a disordered N‐terminal, hence blocking specific interactions and supporting the experimental differences observed. Such distinct structural features must be considered when targeting capsid proteins in drug development. [ABSTRACT FROM AUTHOR]

The emergence of resistance to current antimalarial drugs poses a significant challenge in the fight against malaria. This study aimed to investigate the in vivo antiplasmodial potential of the aqueous extract of fresh and dried leaves of A3 in Plasmodium berghei‐infected (P. berghei) mice. A 4‐day suppressive test was conducted, with infected BALB/c mice receiving artesunate and A3 extracts. The results showed that the tested doses of A3 attenuated the elevation of parasitemia induced by P. berghei, particularly at the dose of 400 mg/kg, and improved hematological indices. Computational techniques, including molecular docking, binding free energy calculations, and ADMET predictions, identified several bioactive compounds in A3 with promising inhibitory potential against lysyl‐tRNA synthetases and Dihydrofolate reductase (DHFR), the crucial enzymes targeted by antimalarial drugs. In this paper, Friedelin, Bauerenol, Epifriedelanol, Alpha‐Amyrenone, Stigmasterol, and beta‐Amyrin acetate were top‐ranked, having docking scores from −10.6 to −9.9 kcal/mol, compared with the −9.4 and −7.1 kcal/mol demonstrated by artesunate and chloroquine, respectively, as standard ligands. Also, it was shown that docking score from the Lysyl‐tRNA protein target (4YCV) ranged from −9.5 to −7.8 kcal/mol in comparison to artesunate (8.1 kcal/mol) and chloroquine (5.6 kcal/mol). The results suggest that the identified compounds in A3 could serve as potential candidates for the development of new anti‐malarial agents. [ABSTRACT FROM AUTHOR]

This study proposes a structure-preserving evolutionary framework to find a semi-analytical approximate solution for a nonlinear cervical cancer epidemic (CCE) model. The underlying CCE model lacks a closed-form exact solution. Numerical solutions obtained through traditional finite difference schemes do not ensure the preservation of the model's necessary properties, such as positivity, boundedness, and feasibility. Therefore, the development of structure-preserving semi-analytical approaches is always necessary. This research introduces an intelligently supervised computational paradigm to solve the underlying CCE model's physical properties by formulating an equivalent unconstrained optimization problem. Singularity-free safe Padé rational functions approximate the mathematical shape of state variables, while the model's physical requirements are treated as problem constraints. The primary model of the governing differential equations is imposed to minimize the error between approximate solutions. An evolutionary algorithm, the Genetic Algorithm with Multi-Parent Crossover (GA-MPC), executes the optimization task. The resulting method is the Evolutionary Safe Padé Approximation (ESPA) scheme. The proof of unconditional convergence of the ESPA scheme on the CCE model is supported by numerical simulations. The performance of the ESPA scheme on the CCE model is thoroughly investigated by considering various orders of non-singular Padé approximants. [ABSTRACT FROM AUTHOR]

This article provides recommendations for analyzing transcriptomic data in allergy research. Transcriptomic data analysis focuses on gene expression at the RNA transcript level and is important for identifying biomarkers and understanding the mechanisms of allergic diseases. The article suggests various normalization methods, techniques for addressing technical variability, statistical models for differential gene expression analysis, and methods for dimensionality reduction and feature selection. It emphasizes the need for rigorous methodology and result interpretation to support personalized medicine and improve treatment strategies. Monte Carlo methods, dimensionality reduction techniques, feature selection methods, and machine learning methods can enhance the accuracy and credibility of transcriptomic data analysis in the study of allergic diseases. [Extracted from the article]

With protein databases growing rapidly due to advances in structural and computational biology, the ability to accurately align and rapidly search protein structures has become essential for biological research. In response to the challenge posed by vast protein structure repositories, GTalign offers an innovative solution to protein structure alignment and search—an algorithm that achieves optimal superposition at high speeds. Through the design and implementation of spatial structure indexing, GTalign parallelizes all stages of superposition search across residues and protein structure pairs, yielding rapid identification of optimal superpositions. Rigorous evaluation across diverse datasets reveals GTalign as the most accurate among structure aligners while presenting orders of magnitude in speedup at state-of-the-art accuracy. GTalign's high speed and accuracy make it useful for numerous applications, including functional inference, evolutionary analyses, protein design, and drug discovery, contributing to advancing understanding of protein structure and function. GTalign introduces spatial structure indexing for accelerated and deep superposition search and protein alignment derivation. Its parallel algorithms facilitate massive protein similarity searches, offering speed and accuracy advantages. [ABSTRACT FROM AUTHOR]

The valorization of lignin, a currently underutilized component of lignocellulosic biomass, has attracted attention to promote a stable and circular bioeconomy. Successful approaches including thermochemical, biological, and catalytic lignin depolymerization have been demonstrated, enabling opportunities for lignino‐refineries and lignocellulosic biorefineries. Although significant progress in lignin valorization has been made, this review describes unexplored opportunities in chemical and biological routes for lignin depolymerization and thereby contributes to economically and environmentally sustainable lignin‐utilizing biorefineries. This review also highlights the integration of chemical and biological lignin depolymerization and identifies research gaps while also recommending future directions for scaling processes to establish a lignino‐chemical industry. [ABSTRACT FROM AUTHOR]

The SARS-CoV-2 pandemic has brought molecular biology and genomic sequencing into the public consciousness and lexicon. With an emphasis on rapid turnaround, genomic data informed both diagnostic and surveillance decisions for the current pandemic at a previously unheard-of scale. The surge in the submission of genomic data to publicly available databases proved essential as comparing different genome sequences offers a wealth of knowledge, including phylogenetic links, modes of transmission, rates of evolution, and the impact of mutations on infection and disease severity. However, the scale of the pandemic has meant that sequencing runs are rarely repeated due to limited sample material and/or the availability of sequencing resources, resulting in the upload of some imperfect runs to public repositories. As a result, it is crucial to investigate the data obtained from these imperfect runs to determine whether the results are reliable prior to depositing them in a public database. Numerous studies have identified a variety of sources of contamination in public next-generation sequencing (NGS) data as the number of NGS studies increases along with the diversity of sequencing technologies and procedures. For this study, we conducted an in silico experiment with known SARS-CoV-2 sequences produced from Oxford Nanopore Technologies sequencing to investigate the effect of contamination on lineage calls and single nucleotide variants (SNVs). A contamination threshold below which runs are expected to generate accurate lineage calls and maintain genome-relatedness and integrity was identified. Together, these findings provide a benchmark below which imperfect runs may be considered robust for reporting results to both stakeholders and public repositories and reduce the need for repeat or wasted runs. Author summary: Large-scale genomic comparisons provide a wealth of knowledge, including modes of transmission, rates of evolution, and the impact of mutations on infection, disease severity, and treatment effectiveness. As a result, the public release of genomic data has proven crucial to response of the SARS-CoV-2 pandemic. However, studies continue to show that some of the genomic data in public repositories are contaminated from a variety of sources. For instance, the rapid response to the SARS-CoV-2 pandemic prevented many sequencing runs from being repeated, resulting in the occasional upload of imperfect runs to public repositories. It is of note that when genomic data is contaminated, both scientific decisions/studies and public health measures may be compromised. To identify genome contamination threshold(s) for SARS-CoV-2 sequences generated by Nanopore sequencing, computational biology techniques were used to generate artificially subsampled and contaminated libraries. This is the first study of its kind and we hope that the results obtained provide a starting point to investigate and report contamination for groups producing and analyzing NGS data. [ABSTRACT FROM AUTHOR]

In the world, cancer is listed as the second leading cause of death. Breast cancer is one of the types that affects women more often than men, and because it has a high mortality rate, the early detection for breast cancer is crucial. The demand for early breast cancer diagnosis and detection has led to a number of creative research avenues in recent years. But even if artificial intelligence techniques have improved in precision, their exactness still has to be increased to allow for their inevitable implementation in practical applications. This paper provides a Salp Swarm and Grey Wolf Optimization-based technique for diagnosing breast cancer that is inspired by nature. Data analysis for breast cancer was done using both SVM and KNN algorithms. For the purpose of diagnosis, we made use of the Wisconsin Breast Cancer Dataset (WBCD). The study also describes the proposed model's actual implementation in the field of computational biology, together with its characteristics, assessments, evaluations, and conclusions. Specificity, precision, F1-score, recall, and accuracy were some of the metrics used to evaluate how well the approach in question performed. When used on the WBCD-dataset, the proposed SSA-GWO model had an accuracy of 99.42%. The outcomes of the actual applications demonstrate the suggested hybrid algorithm's applicability to difficult situations involving unidentified search spaces. [ABSTRACT FROM AUTHOR]

The hypothalamus in the mammalian brain is responsible for regulating functions associated with survival and reproduction representing a complex set of highly interconnected, yet anatomically and functionally distinct, sub-regions. It remains unclear what factors drive the spatial organization of sub-regions within the hypothalamus. One potential factor may be structural connectivity of the network that promotes efficient function with well-connected sub-regions placed closer together geometrically, i.e., the strongest axonal signal transferred through the shortest geometrical distance. To empirically test for such efficiency, we use hypothalamic data derived from the Allen Mouse Brain Connectivity Atlas, which provides a structural connectivity map of mouse brain regions derived from a series of viral tracing experiments. Using both cost function minimization and comparison with a weighted, sphere-packing ensemble, we demonstrate that the sum of the distances between hypothalamic sub-regions are not close to the minimum possible distance, consistent with prior whole brain studies. However, if such distances are weighted by the inverse of the magnitude of the connectivity, their sum is among the lowest possible values. Specifically, the hypothalamus appears within the top 94th percentile of neural efficiencies of randomly packed configurations and within one standard deviation of the median efficiency when packings are optimized for maximal neural efficiency. Our results, therefore, indicate that a combination of geometrical and topological constraints help govern the structure of the hypothalamus. [ABSTRACT FROM AUTHOR]

The categorization of human diseases is mainly based on the affected organ system and phenotypic characteristics. This is limiting the view to the pathological manifestations, while it neglects mechanistic relationships that are crucial to develop therapeutic strategies. This work aims to advance the understanding of diseases and their relatedness beyond traditional phenotypic views. Hence, the similarity among 502 diseases is mapped using six different data dimensions encompassing molecular, clinical, and pharmacological information retrieved from public sources. Multiple distance measures and multi‐view clustering are used to assess the patterns of disease relatedness. The integration of all six dimensions into a consensus map of disease relationships reveals a divergent disease view from the International Classification of Diseases (ICD), emphasizing novel insights offered by a multi‐view disease map. Disease features such as genes, pathways, and chemicals that are enriched in distinct disease groups are identified. Finally, an evaluation of the top similar diseases of three candidate diseases common in the Western population shows concordance with known epidemiological associations and reveals rare features shared between Type 2 diabetes (T2D) and Alzheimer's disease. A revision of disease relationships holds promise for facilitating the reconstruction of comorbidity patterns, repurposing drugs, and advancing drug discovery in the future. [ABSTRACT FROM AUTHOR]

Phytochemicals have a long and successful history in drug discovery. With recent advancements in analytical techniques and methodologies, discovering bioactive leads from natural compounds has become easier. Computational techniques like molecular docking, QSAR modelling and machine learning, and network pharmacology are among the most promising new tools that allow researchers to make predictions concerning natural products' potential targets, thereby guiding experimental validation efforts. Additionally, approaches like LC-MS or LC-NMR speed up compound identification by streamlining analytical processes. Integrating structural and computational biology aids in lead identification, thus providing invaluable information to understand how phytochemicals interact with potential targets in the body. An emerging computational approach is machine learning involving QSAR modelling and deep neural networks that interrelate phytochemical properties with diverse physiological activities such as antimicrobial or anticancer effects. [ABSTRACT FROM AUTHOR]

The international AfroBiotech conference series, launched in 2019 in Atlanta, GA, USA, is an emerging platform to foster innovation, promote inclusivity, and highlight the contributions of Black scientists in the biotechnology sector. Led by scientists and engineers from the African Diaspora community, these conferences serve as a vital forum for networking, sharing groundbreaking research, and addressing health disparities, thereby advancing both scientific excellence and social equity in biotech. This review discusses the mission and impact of AfroBiotech and highlights similar conferences, particularly in regard to addressing the legacy of disparities in science and engineering in the US. An in-depth overview of AfroBiotech attendees, over 100 per year, from diverse sectors including academia, industry, and government is provided. This review also highlights the research presented at AfroBiotech by scientists in regenerative engineering, medical biotechnology, genomics and genetics, computational and systems biology, nanotechnology and biochemistry, and immunological and microbiome engineering. Activities to support an inclusive workforce are also discussed. Through fostering an inclusive environment, AfroBiotech is on a trajectory to inspire a new generation of scientists and pave the way for a diverse and equitable biotechnological future. [ABSTRACT FROM AUTHOR]

Carcinogenesis is closely related to the expression, maintenance, and stability of DNA. These processes are regulated by one-carbon metabolism (1CM), which involves several vitamins of the complex B (folate, B2, B6, and B12), whereas alcohol disrupts the cycle due to the inhibition of folate activity. The relationship between nutrients related to 1CM (all aforementioned vitamins and alcohol) in breast cancer has been reviewed. The interplay of genes related to 1CM was also analyzed. Single nucleotide polymorphisms located in those genes were selected by considering the minor allele frequency in the Caucasian population and the linkage disequilibrium. These genes were used to perform several in silico functional analyses (considering corrected p-values < 0.05 as statistically significant) using various tools (FUMA, ShinyGO, and REVIGO) and databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and GeneOntology (GO). The results of this study showed that intake of 1CM-related B-complex vitamins is key to preventing breast cancer development and survival. Also, the genes involved in 1CM are overexpressed in mammary breast tissue and participate in a wide variety of biological phenomena related to cancer. Moreover, these genes are involved in alterations that give rise to several types of neoplasms, including breast cancer. Thus, this study supports the role of one-carbon metabolism B-complex vitamins and genes in breast cancer; the interaction between both should be addressed in future studies. [ABSTRACT FROM AUTHOR]

Functional microbial communities producing β-glucosidases differentially regulate the expression of glucose/non-glucose-tolerant β-glucosidases in response to carbon catabolite repression (CCR), which is a common phenomenon. To better understand their biological roles in a composting environment, four representative β-glucosidase genes were cloned from the metatranscriptome of the compost for expression and characterization. BGLA contains conserved sites Trp168 and Leu173, associated with glucose tolerance. In the presence of 100 mM glucose, BGLA's hydrolysis activity increases by 80%. BGLB also contains Trp168 and Leu173; however, its hydrolysis activity is significantly inhibited by glucose. On the contrary, Trp168 of BGLC was replaced by Phe, yet its hydrolysis activity increased by 20% in the presence of 25 mM glucose. However, BGLD did not exhibit any enzymatic activity. Molecular docking and dynamic simulations revealed the structural basis of glucose tolerance. Metatranscriptomic analysis of the four genes showed that functional microbial communities upregulated or downregulated gene expression in response to different CCR environments to maintain overall carbon metabolic balance. These findings demonstrate that the enzymatic activity characteristics of individual glucose/non-glucose-tolerant β-glucosidases are consistent with their transcriptional regulation under CCR in the complex composting environment. [Display omitted] • Four new GH1 family β-glucosidases cloned from compost. • BGLA activity increased by 80% with added glucose. • Computational biology elucidates β-glucosidase glucose tolerance mechanism. • Glucose tolerance of β-glucosidases correlates with their expression level. [ABSTRACT FROM AUTHOR]

This article shows that the gas-phase entropy of molecules is proportional to the area of the molecules, with corrections for the different curvatures of the molecular surface. The ability to estimate gas-phase entropy by the area law also allows us to calculate molecular entropy faster and more accurately than currently popular methods of estimating molecular entropy with harmonic oscillator approximation. The speed and accuracy of our method will open up new possibilities for the explicit inclusion of entropy in various computational biology methods. [ABSTRACT FROM AUTHOR]

Diabetes, a chronic disease characterized by insufficient insulin production, affects approximately 422 million people globally. While researchers have achieved notable advancements in identifying new blood and tissue-based biomarkers related to diabetes, a more detailed and comprehensive investigation is needed. In this study, we conducted a systematic bioinformatics analysis using the Diabetes Complications Early Warning database to examine these associations. First, we identified the association of glucose-related markers with major markers related to blood lipid metabolism, renal function, and liver function. Second, we applied bioinformatics analysis to screen for major markers and diseases capable of categorizing subpopulations within the diabetic population and identified twelve blood markers and four disease markers. We discover that complete blood count (CBC) and blood chemistry (BC) biomarkers of different diabetic patients exhibit subtle changes in their performances. This may be attributed to their differential diabetic complications. Our study enhances the understanding of diabetes mechanisms and provides new avenues for developing precision medicine and therapeutic strategies. [ABSTRACT FROM AUTHOR]

The rapid advancement of sequencing technologies has led to the identification of numerous mutations in cancer genomes, many of which are variants of unknown significance (VUS). Computational models are increasingly being used to predict the functional impact of these mutations, in both coding and noncoding regions. Integration of these models with emerging genomic datasets will refine our understanding of mutation effects and guide clinical decision making. Future advancements in modeling protein interactions and transcriptional regulation will further enhance our ability to interpret VUS. Periodic incorporation of these developments into VUS reclassification practice has the potential to significantly improve personalized cancer care. [ABSTRACT FROM AUTHOR]

The article discusses the development of optimized dCas9 programmable transcriptional activators (PTAs) for plants. The researchers aimed to improve the efficiency of PTAs by replacing the VP64 domain with plant-derived activation domains (ADs). They tested various ADs in protoplasts from Arabidopsis and Seteria species and found that ADs such as DREB2, AvrXa10, DOF1, AtHSFA6b, and DREB1 showed transactivation activity comparable or better than VP64. They also tested the ability of strong ADs to activate gene expression from endogenous promoters in Arabidopsis and found significant increases in expression. Overall, the study provides insights into improving gene expression in plants for crop engineering purposes. Additionally, the authors present a new PTA called MoonTag, which uses nanobody: epitope interactions to recruit ADs to the dCas9 protein. They demonstrate that the MoonTag PTA can overexpress the FT gene in transgenic plants, resulting in an early-flowering phenotype. The authors also explore the synergism between targeting sgRNAs at both a core promoter and enhancer region, showing that PTA-mediated enhancer activation can retain the original genomic architecture and chromatin state. The study highlights the potential use of PTAs in mapping enhancer regions in plant genomes. [Extracted from the article]

The epigenetic regulation of N6-methyladenosine (m6A) has attracted considerable interest in tumor research, but the potential roles of m6A regulator-related genes, remain largely unknown within the context of gastric cancer (GC) and tumor microenvironment (TME). Here, a comprehensive strategy of data mining and computational biology utilizing multiple datasets based on 28 m6A regulators (including novel anti-readers) was employed to identify m6A regulator-related genes and patterns and elucidate their underlying mechanisms in GC. Subsequently, a scoring system was constructed to evaluate individual prognosis and immunotherapy response. Three distinct m6A regulator-related patterns were identified through the unsupervised clustering of 56 m6A regulator-related genes (all significantly associated with GC prognosis). TME characterization revealed that these patterns highly corresponded to immune-inflamed, immune-excluded, and immune-desert phenotypes, and their TME characteristics were highly consistent with different clinical outcomes and biological processes. Additionally, an m6A-related scoring system was developed to quantify the m6A modification pattern of individual samples. Low scores indicated high survival rates and high levels of immune activation, whereas high scores indicated stromal activation and tumor malignancy. Furthermore, the m6A-related scores were correlated with tumor mutation loads and various clinical traits, including molecular or histological subtypes and clinical stage or grade, and the score had predictive values across all digestive system tumors and even in all tumor types. Notably, a low score was linked to improved responses to anti-PD-1/L1 and anti-CTLA4 immunotherapy in three independent cohorts. This study has expanded the important role of m6A regulator-related genes in shaping TME diversity and clinical/biological traits of GC. The developed scoring system could help develop more effective immunotherapy strategies and personalized treatment guidance. [ABSTRACT FROM AUTHOR]

The complexity of cancer development involves intricate interactions among multiple biomarkers, such as gene-environment interactions. Utilizing microarray gene expression profile data for cancer classification is anticipated to be effective, thus drawing considerable interest in the fields of bioinformatics and computational biology. Due to the characteristics of genomic data, problems of high-dimensional interactions and noise interference do exist during the analysis process. When building cancer diagnosis models, we often face the dilemma of model adaptation errors due to an imbalance of data types. To mitigate the issues, we apply the SMOTE-Tomek procedure to rectify the imbalance problem. Following this, we utilize the overlapping group screening method alongside a binary logistic regression model to integrate gene pathway information, facilitating the identification of significant biomarkers associated with clinically imbalanced cancer or normal outcomes. Simulation studies across different imbalanced rates and gene structures validate our proposed method's effectiveness, surpassing common machine learning techniques in terms of classification prediction accuracy. We also demonstrate that prediction performance improves with SMOTE-Tomek treatment compared to no imbalance treatment and SMOTE treatment across various imbalance rates. In the real-world application, we integrate clinical and gene expression data with prior pathway information. We employ SMOTE-Tomek and our proposed methods to identify critical biomarkers and gene-environment interactions linked to the imbalanced binary outcomes (cancer or normal) in patients from the Cancer Genome Atlas datasets of lung adenocarcinoma and breast invasive carcinoma. Our proposed method consistently achieves satisfactory classification accuracy. Additionally, we have identified biomarkers indicative of gene-environment interactions relevant to cancer and have provided corresponding estimates of odds ratios. Moreover, in high-dimensional imbalanced data, for achieving good prediction results, we recommend considering the order of balancing processing and feature screening. [ABSTRACT FROM AUTHOR]

Although the disease caused by chikungunya virus (CHIKV) is of great interest to public health organizations around the world, there are still no authorized antivirals for its treatment. Previously, dihalogenated anti-CHIKV compounds derived from L-tyrosine (dH-Y) were identified as being effective against in vitro infection by this virus, so the objective of this study was to determine the mechanisms of its antiviral action. Six dH-Y compounds (C1 to C6) dihalogenated with bromine or chlorine and modified in their amino groups were evaluated by different in vitro antiviral strategies and in silico tools. When the cells were exposed before infection, all compounds decreased the expression of viral proteins; only C4, C5 and C6 inhibited the genome; and C1, C2 and C3 inhibited infectious viral particles (IVPs). Furthermore, C1 and C3 reduce adhesion, while C2 and C3 reduce internalization, which could be related to the in silico interaction with the fusion peptide of the E1 viral protein. Only C3, C4, C5 and C6 inhibited IVPs when the cells were exposed after infection, and their effect occurred in late stages after viral translation and replication, such as assembly, and not during budding. In summary, the structural changes of these compounds determine their mechanism of action. Additionally, C3 was the only compound that inhibited CHIKV infection at different stages of the replicative cycle, making it a compound of interest for conversion as a potential drug. [ABSTRACT FROM AUTHOR]

Mathematical models of biochemical reaction networks are an important and emerging tool for the study of cell signaling networks involved in disease processes. One promising potential application of such mathematical models is the study of how disease-causing mutations promote the signaling phenotype that contributes to the disease. It is commonly assumed that one must have a thorough characterization of the network readily available for mathematical modeling to be useful, but we hypothesized that mathematical modeling could be useful when there is incomplete knowledge and that it could be a tool for discovery that opens new areas for further exploration. In the present study, we first develop a mechanistic mathematical model of a G-protein coupled receptor signaling network that is mutated in almost all cases of uveal melanoma and use model-driven explorations to uncover and explore multiple new areas for investigating this disease. Modeling the two major, mutually-exclusive, oncogenic mutations (Gαq/11 and CysLT2R) revealed the potential for previously unknown qualitative differences between seemingly interchangeable disease-promoting mutations, and our experiments confirmed oncogenic CysLT2R was impaired at activating the FAK/YAP/TAZ pathway relative to Gαq/11. This led us to hypothesize that CYSLTR2 mutations in UM must co-occur with other mutations to activate FAK/YAP/TAZ signaling, and our bioinformatic analysis uncovers a role for co-occurring mutations involving the plexin/semaphorin pathway, which has been shown capable of activating this pathway. Overall, this work highlights the power of mechanism-based computational systems biology as a discovery tool that can leverage available information to open new research areas. [ABSTRACT FROM AUTHOR]

Ischemic stroke, a significant threat to human life and health, refers to a class of conditions where brain tissue damage is induced following decreased cerebral blood flow. The incidence of ischemic stroke has been steadily increasing globally, and its disease mechanisms are highly complex and involve a multitude of biological mechanisms at various scales from genes all the way to the human body system that can affect the stroke onset, progression, treatment, and prognosis. To complement conventional experimental research methods, computational systems biology modeling can integrate and describe the pathogenic mechanisms of ischemic stroke across multiple biological scales and help identify emergent modulatory principles that drive disease progression and recovery. In addition, by running virtual experiments and trials in computers, these models can efficiently predict and evaluate outcomes of different treatment methods and thereby assist clinical decision-making. In this review, we summarize the current research and application of systems-level computational modeling in the field of ischemic stroke from the multiscale mechanism-based, physics-based and omics-based perspectives and discuss how modeling-driven research frameworks can deliver insights for future stroke research and drug development. [ABSTRACT FROM AUTHOR]

Developmental biology—the study of the processes by which cells, tissues, and organisms develop and change over time—has entered a new golden age. After the molecular genetics revolution in the 80s and 90s and the diversification of the field in the early 21st century, we have entered a phase when powerful technologies provide new approaches and open unexplored avenues. Progress in the field has been accelerated by advances in genomics, imaging, engineering, and computational biology and by emerging model systems ranging from tardigrades to organoids. We summarize how revolutionary technologies have led to remarkable progress in understanding animal development. We describe how classic questions in gene regulation, pattern formation, morphogenesis, organogenesis, and stem cell biology are being revisited. We discuss the connections of development with evolution, self-organization, metabolism, time, and ecology. We speculate how developmental biology might evolve in an era of synthetic biology, artificial intelligence, and human engineering. As developmental biology enters a new golden age, this Review summarizes how revolutionary technologies have been integral to the advancement of the field over the last five decades and speculates on how developmental biology might evolve with the changing landscape of cutting-edge technology. [ABSTRACT FROM AUTHOR]

Fibril formation is a key feature in neurodegenerative diseases like Alzheimer's, Parkinson's, and systemic amyloidosis. Polyphenols, found in plant-based foods, show promise in inhibiting fibril formation and disrupting disease progression. The ability of polyphenols to break the amyloid fibrils of many disease-linked proteins has been tested in numerous studies. Polyphenols have their distinctive mechanism of action. They behave differently on various events in the aggregation pathway. Their action also differs for different proteins. Some polyphenols only inhibit the formation of fibrils whereas others break the preformed fibrils. Some break the fibrils into smaller species, and some change them to other morphologies. This article delves into the intricate molecular mechanisms underlying the inhibitory effects of polyphenols on fibrillogenesis, shedding light on their interactions with amyloidogenic proteins and the disruption of fibril assembly pathways. However, addressing the challenges associated with solubility, stability, and bioavailability of polyphenols is crucial. The current strategies involve nanotechnology to improve the solubility and bioavailability, thus showing the potential to enhance the efficacy of polyphenols as therapeutics. Advancements in structural biology, computational modeling, and biophysics have provided insights into polyphenol–fibril interactions, offering hope for novel therapies for neurodegenerative diseases and amyloidosis. [ABSTRACT FROM AUTHOR]

Summary Multiple sequence alignment is an important problem in computational biology with applications that include phylogeny and the detection of remote homology between protein sequences. UPP is a popular software package that constructs accurate multiple sequence alignments for large datasets based on ensembles of hidden Markov models (HMMs). A computational bottleneck for this method is a sequence-to-HMM assignment step, which relies on the precise computation of probability scores on the HMMs. In this work, we show that we can speed up this assignment step significantly by replacing these HMM probability scores with alternative scores that can be efficiently estimated. Our proposed approach utilizes a multi-armed bandit algorithm to adaptively and efficiently compute estimates of these scores. This allows us to achieve similar alignment accuracy as UPP with a significant reduction in computation time, particularly for datasets with long sequences. Availability and implementation The code used to produce the results in this paper is available on GitHub at: https://github.com/ilanshom/adaptiveMSA. [ABSTRACT FROM AUTHOR]

Motivation Gene regulatory networks (GRNs) are vital tools for delineating regulatory relationships between transcription factors and their target genes. The boom in computational biology and various biotechnologies has made inferring GRNs from multi-omics data a hot topic. However, when networks are constructed from gene expression data, they often suffer from false-positive problem due to the transitive effects of correlation. The presence of spurious noise edges obscures the real gene interactions, which makes downstream analyses, such as detecting gene function modules and predicting disease-related genes, difficult and inefficient. Therefore, there is an urgent and compelling need to develop network denoising methods to improve the accuracy of GRN inference. Results In this study, we proposed a novel network denoising method named REverse Network Diffusion On Random walks (RENDOR). RENDOR is designed to enhance the accuracy of GRNs afflicted by indirect effects. RENDOR takes noisy networks as input, models higher-order indirect interactions between genes by transitive closure, eliminates false-positive effects using the inverse network diffusion method, and produces refined networks as output. We conducted a comparative assessment of GRN inference accuracy before and after denoising on simulated networks and real GRNs. Our results emphasized that the network derived from RENDOR more accurately and effectively captures gene interactions. This study demonstrates the significance of removing network indirect noise and highlights the effectiveness of the proposed method in enhancing the signal-to-noise ratio of noisy networks. Availability and implementation The R package RENDOR is provided at https://github.com/Wu-Lab/RENDOR and other source code and data are available at https://github.com/Wu-Lab/RENDOR-reproduce [ABSTRACT FROM AUTHOR]

Adeno-associated viruses 2 (AAV2) are minute viruses renowned for their capacity to infect human cells and akin organisms. They have recently emerged as prominent candidates in the field of gene therapy, primarily attributed to their inherent non-pathogenic nature in humans and the safety associated with their manipulation. The efficacy of AAV2 as gene therapy vectors hinges on their ability to infiltrate host cells, a phenomenon reliant on their competence to construct a capsid capable of breaching the nucleus of the target cell. To enhance their infection potential, researchers have extensively scrutinized various combinatorial libraries by introducing mutations into the capsid, aiming to boost their effectiveness. The emergence of high-throughput experimental techniques, like deep mutational scanning (DMS), has made it feasible to experimentally assess the fitness of these libraries for their intended purpose. Notably, machine learning is starting to demonstrate its potential in addressing predictions within the mutational landscape from sequence data. In this context, we introduce a biophysically-inspired model designed to predict the viability of genetic variants in DMS experiments. This model is tailored to a specific segment of the CAP region within AAV2's capsid protein. To evaluate its effectiveness, we conduct model training with diverse datasets, each tailored to explore different aspects of the mutational landscape influenced by the selection process. Our assessment of the biophysical model centers on two primary objectives: (i) providing quantitative forecasts for the log-selectivity of variants and (ii) deploying it as a binary classifier to categorize sequences into viable and non-viable classes. [ABSTRACT FROM AUTHOR]

Tumor mutational signatures have gained prominence in cancer research, yet the lack of standardized methods hinders reproducibility and robustness. Leveraging colorectal cancer (CRC) as a model, we explored the influence of computational parameters on mutational signature analyses across 230 CRC cell lines and 152 CRC patients. Results were validated in three independent datasets: 483 endometrial cancer patients stratified by mismatch repair (MMR) status, 35 lung cancer patients by smoking status and 12 patient-derived organoids (PDOs) annotated for colibactin exposure. Assessing various bioinformatic tools, reference datasets and input data sizes including whole genome sequencing, whole exome sequencing and a pan-cancer gene panel, we demonstrated significant variability in the results. We report that the use of distinct algorithms and references led to statistically different results, highlighting how arbitrary choices may induce variability in the mutational signature contributions. Furthermore, we found a differential contribution of mutational signatures between coding and intergenic regions and defined the minimum number of somatic variants required for reliable mutational signature assignment. To facilitate the identification of the most suitable workflows, we developed Comparative Mutational Signature analysis on Coding and Extragenic Regions (CoMSCER), a bioinformatic tool which allows researchers to easily perform comparative mutational signature analysis by coupling the results from several tools and public reference datasets and to assess mutational signature contributions in coding and non-coding genomic regions. In conclusion, our study provides a comparative framework to elucidate the impact of distinct computational workflows on mutational signatures. [ABSTRACT FROM AUTHOR]

This paper presents a neutrosophic inference model for bioinformatics. The model is used to develop a system for accurate comparisons of human nucleic acids, where the new nucleic acid is compared to a database of old nucleic acids. The comparisons are analyzed in terms of accuracy, certainty, uncertainty, neutrality, and bias. The proposed system achieves good results and provides a reliable standard for future comparisons. It highlights the potential of neutrosophic inference models in bioinformatics applications. Data mining and bioinformatics play a crucial role in computational biology, with applications in scientific research and industrial development. Biological analysts rely on specialized tools and algorithms to collect, store, categorize, and analyze large volumes of unstructured data. Data mining techniques are used to extract valuable information from this data, aiding in the development of new therapies and understanding genetic relationships between organisms. Recent advancements in bioinformatics include gene expression tools, Bio sequencing, and Bio databases, which facilitate the extraction and analysis of vital biological information. These technologies contribute to the analysis of big data, identification of key bioinformatics insights, and generation of new biological knowledge. Data collection, analysis, and interpretation in this field involves the use of modern technologies such as cloud computing, machine learning, and artificial intelligence, enabling more efficient and accurate results. Ultimately, data mining and bioinformatics enhance our understanding of genetic relationships, aid in developing new therapies, and improve healthcare outcomes. [ABSTRACT FROM AUTHOR]

The present study is aimed to address the challenge of wound age estimation in forensic science by identifying reliable genetic markers using low-cost and high-precision second-generation sequencing technology. A total of 54 Sprague-Dawley rats were randomly assigned to a control group or injury groups, with injury groups being further divided into time points (4 h, 8 h, 12 h, 16 h, 20 h, 24 h, 28 h, and 32 h after injury, n = 6) to establish rat skeletal muscle contusion models. Gene expression data were obtained using second-generation sequencing technology, and differential gene expression analysis, weighted gene co-expression network analysis (WGCNA) and time-dependent expression trend analysis were performed. A total of six sets of biomarkers were obtained: differentially expressed genes at adjacent time points (127 genes), co-expressed genes most associated with wound age (213 genes), hub genes exhibiting time-dependent expression (264 genes), and sets of transcription factors (TF) corresponding to the above sets of genes (74, 87, and 99 genes, respectively). Then, random forest (RF), support vector machine (SVM) and multilayer perceptron (MLP), were constructed for wound age estimation from the above gene sets. The results estimated by transcription factors were all superior to the corresponding hub genes, with the transcription factor group of WGCNA performed the best, with average accuracy rates of 96% for three models' internal testing, and 91.7% for the highest external validation. This study demonstrates the advantages of the indicator screening system based on second-generation sequencing technology and transcription factor level for wound age estimation. [ABSTRACT FROM AUTHOR]

The increasing emergence of the time-series single-cell RNA sequencing (scRNA-seq) data, inferring developmental trajectory by connecting transcriptome similar cell states (i.e., cell types or clusters) has become a major challenge. Most existing computational methods are designed for individual cells and do not take into account the available time series information. We present IDTI based on the Increment of Diversity for Trajectory Inference, which combines time series information and the minimum increment of diversity method to infer cell state trajectory of time-series scRNA-seq data. We apply IDTI to simulated and three real diverse tissue development datasets, and compare it with six other commonly used trajectory inference methods in terms of topology similarity and branching accuracy. The results have shown that the IDTI method accurately constructs the cell state trajectory without the requirement of starting cells. In the performance test, we further demonstrate that IDTI has the advantages of high accuracy and strong robustness. [ABSTRACT FROM AUTHOR]

Mitochondrial aging has been proposed to be involved in a variety of neurodegenerative disorders, such as Parkinson's disease. Here, we explore the impact of multiple branching junctions in axons on the mean age of mitochondria and their age density distributions in demand sites. The study examined mitochondrial concentration, mean age, and age density distribution in relation to the distance from the soma. We developed models for a symmetric axon containing 14 demand sites and an asymmetric axon containing 10 demand sites. We investigated how the concentration of mitochondria changes when an axon splits into two branches at the branching junction. Additionally, we studied whether mitochondrial concentrations in the branches are affected by what proportion of mitochondrial flux enters the upper branch versus the lower branch. Furthermore, we explored whether the distributions of mitochondrial mean age and age density in branching axons are affected by how the mitochondrial flux splits at the branching junction. When the mitochondrial flux is unevenly split at the branching junction of an asymmetric axon, with a greater proportion of the flux entering the longer branch, the average age of mitochondria (system age) in the axon increases. Our findings elucidate the effects of axonal branching on the mitochondrial age. [ABSTRACT FROM AUTHOR]