1210 papers
Bioinformatics sits at the exciting intersection where biology meets data science, using powerful computer tools to decode the vast complexity of living systems. From mapping the human genome to tracking how viruses evolve, this field transforms raw biological information into actionable insights that drive modern medicine and research forward without requiring a supercomputer to understand the basics.
On Gist.Science, we ensure you never miss a breakthrough by processing every new preprint in this category directly from bioRxiv. Our team provides both plain-language explanations and detailed technical summaries for each paper, making cutting-edge discoveries accessible to everyone regardless of their background.
Below are the latest bioinformatics papers added from bioRxiv, ready for you to explore with clarity and depth.
This study demonstrates that RNA foundation models significantly improve the generalizability of endometriosis classification across independent cohorts compared to traditional baselines, while a novel interpretability method (CA-IG) reveals stable, biologically plausible gene-level signatures that overcome the instability of standard explainability approaches.
PhyMapNet is a computationally efficient, phylogeny-guided Bayesian framework that infers robust and interpretable microbiome networks by integrating evolutionary information and employing a tuning-free consensus strategy to overcome data sparsity and hyperparameter sensitivity.
By organizing the largest single-cell data competition to date and analyzing over 27,000 submissions on a novel longitudinal multimodal benchmark, this study identifies superior modeling strategies and provides reproducible tools that advance the prediction of gene regulatory patterns across chromatin, transcriptomic, and proteomic layers.
The paper introduces STRATA, a differential-geometric framework that transforms spatial transcriptomics from discrete cell clustering into continuous field analysis, revealing that human skin melanoma homogenizes rather than abolishes the spatial coupling architecture of transcription factor regulons.
TopoMetry is a unified, geometry-aware framework that systematically learns and evaluates intrinsic coordinate systems from single-cell data to generate high-fidelity spectral scaffolds, outperforming standard workflows in preserving biological structure and revealing hidden cellular diversity.
This study demonstrates that the machine learning-based tool MS2Rescore significantly enhances peptide identification rates and taxonomic specificity in metaproteomics, enabling stricter false discovery rate thresholds and more reliable downstream taxonomic analysis compared to traditional workflows.
The paper introduces Floco, a novel method that improves copy number calling accuracy on genome graphs by combining node-level read depth probabilities with a network flow formulation solved via integer linear programming, thereby overcoming the limitations of traditional linear reference mapping and simple graph node predictions.
The authors present CoMR, an integrative scoring pipeline that combines targeting prediction, homology searches, and phylogenetic analysis to significantly improve the accuracy of mitochondrial proteome reconstruction across diverse eukaryotes compared to standalone predictors.
The paper proposes RSTG, a robust autoencoder utilizing beta-divergence-based variational inference to generate high-quality synthetic spatial transcriptomics data that effectively recovers cellular positions and maintains stability against noise, outliers, and batch effects across multiple datasets and technologies.
The paper introduces EnzPlacer, a contrastive learning algorithm that predicts the broader functional context (EC numbers 1–3) of enzymes with unknown specific functions (EC 4) by accurately placing them within known functional spaces to generate testable hypotheses for experimental characterization.