1200 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 utilizes multi-organ MRI data from 70,000 individuals to demonstrate that local and global imaging patterns can effectively quantify biological ageing, revealing significant associations with chronic diseases and lifestyle factors while establishing a framework for personalized health risk stratification.
This paper presents a minimal mathematical model driven by inflammation and demyelination that successfully reproduces the relapsing-remitting dynamics of multiple sclerosis through Hopf bifurcation and aligns with experimental data on contrast-enhancing lesions.
This paper introduces TPCAV, a novel concept attribution framework that adapts and enhances the TCAV method with PCA-based decorrelation to provide robust, input-agnostic interpretations of diverse genomics deep learning models, enabling the analysis of both traditional DNA motifs and complex biological features like chromatin states and repetitive elements.
The paper introduces GeoARG, a geometry-enhanced protein language modeling framework that overcomes the limitations of sequence homology to successfully identify thousands of evolutionarily distant and structurally conserved antibiotic resistance genes in metagenomic data.
This paper introduces a geometry-aware framework that distinguishes interface-associated enrichment from true spatial localization in ligand-receptor interactions, revealing that tumor cell communication is better characterized as a continuum of spatial constraint rather than discrete communication regimes.
This study demonstrates that simultaneously correcting for both expression-based surrogate variables and genotype-based principal components in differential expression analysis significantly improves cross-dataset replicability and biological recall of known disease genes compared to using either method alone, establishing a combined correction framework as a standard practice for transcriptomic studies with matched genotype data.
This paper introduces *sosta*, an open-source Bioconductor package that enables the direct analysis of multicellular anatomical structures in spatial omics data, shifting the focus from single-cell arrangements to biologically relevant tissue-level organization.
This paper proposes a novel four-layer integrative framework that constructs gene regulatory networks directly from DNA sequences by leveraging information entropy, evolutionary conservation, and deep learning embeddings to bridge nucleotide-level constraints with network-level regulatory logic.
This paper argues that co-expression network analysis offers a more effective and parsimonious framework than traditional differential expression analysis for characterizing microglia, as it reveals context-dependent, concurrent molecular programs that better explain the cell type's functional heterogeneity.
The paper introduces Locat, a novel framework that identifies highly specific marker genes in single-cell transcriptomics by jointly testing for expression enrichment within compact cellular regions and depletion elsewhere, thereby enabling robust, interpretable, and batch-free cross-condition comparisons of cell populations and developmental trajectories.