1205 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 integrating subcellular localization with RNA expression data significantly improves the prediction of protein detectability across human cancers, revealing that widespread RNA-protein discordance is driven by structured biological processes rather than stochastic variation.
CROWN introduces a machine learning-ready dataset of 153,005 high-quality protein-ligand complexes that reconciles the trade-off between structural reliability and data diversity by applying a comprehensive automated pipeline with a novel energy minimization step to the PLInder database, offering a geometry-centric resource for training and benchmarking interaction prediction models.
This paper introduces Claw4Science, a curated dataset and public platform that unifies the fragmented OpenClaw scientific agent ecosystem by cataloging 91 projects and 2,230 skills to facilitate discovery, analysis, and standardized development in bioinformatics and scientific workflows.
This paper evaluates strategies for robustifying Random Forests against response contamination in genomic prediction, finding that data transformation and ranking-based approaches offer the most effective remedies for recovering latent signals in noisy breeding datasets while advising that standard models remain preferable for clean data.
The paper introduces BulkMonSTR, a computational framework that combines STR-specific error modeling with machine learning to accurately detect and distinguish genuine mosaic short tandem repeat mutations from sequencing noise and germline variants in bulk sequencing data, outperforming existing methods across diverse sample types.
This paper introduces a scalable, explainable, and data-efficient framework that reframes protein-protein binding affinity prediction as a metric learning task using fine-tuned protein language models, achieving state-of-the-art performance from sequence alone without requiring 3D structural inputs.
The IMMREP25 benchmark demonstrates that incorporating structural modeling significantly advances the prediction of TCR:pMHC binding for unseen peptides, achieving a macro-AUC_0.1 of 0.60 and marking a notable improvement over previous random-guessing performance on such data.
This paper introduces scRitmo, a probabilistic framework that accurately infers single-cell circadian phases and quantifies biological desynchrony by distinguishing it from technical noise across diverse tissues, organisms, and experimental conditions.
This study presents an integrated computational-experimental framework that successfully identified diverse GLP-1R agonist candidates, including the pentapeptide DPDPE, which exhibits full agonism and dual GLP-1R/GIPR activity, thereby establishing a robust strategy for discovering chemotype-diverse therapeutics against flexible GPCR targets.
This study demonstrates that utilizing the graph-based human pangenome reference significantly improves the accuracy and precision of somatic mutation detection, particularly for individuals of East Asian ancestry, by reducing reference bias and germline contamination compared to traditional linear reference genomes.