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.
DOME Copilot is a scalable, large language model-based solution designed to enhance the transparency and reproducibility of artificial intelligence in life sciences by automatically extracting structured method reports from manuscripts to interpret and annotate global AI literature.
This study addresses the clinical interpretation gap for in-frame indels by calibrating eight computational predictors using a high-confidence dataset and a statistical framework to establish ACMG/AMP-compliant score thresholds, demonstrating their measurable utility while highlighting their currently lower performance compared to missense variant tools.
This study demonstrates that in heterogeneous pan-cancer cohorts treated with immune checkpoint inhibitors, clinical variables significantly outperform and overshadow genomic features like tumor mutational burden in predicting survival, revealing fundamental structural limits to the added value of integrating whole-genome sequencing data into current predictive models.
The paper introduces LagCI, a computational framework that overcomes the limitations of sparse sampling and existing methods to infer robust, time-lagged causal relationships from dense multi-omic time-series data, successfully identifying a vast network of molecular interactions that orchestrate metabolic and immune responses.
This paper demonstrates that incorporating spatial structure into generalized Lotka-Volterra models resolves the discrepancy between theoretical predictions and empirical observations by showing that aggregating microbial abundances across a fragmented landscape, rather than specific biological mechanisms, generates the universal gamma distribution observed in macroecological patterns.
The paper introduces PeptideCLM-2, a chemical language model trained on over 100 million molecules to bridge the computational gap in therapeutic peptide engineering by natively representing complex peptide chemistry and outperforming existing methods in predicting key development endpoints.
This paper presents an agent-guided computational workflow that successfully designed and experimentally validated high-affinity de novo nanobody binders against a novel cancer target, achieving a 39.7% success rate in generating sub-nanomolar to nanomolar affinity candidates without prior structural or antibody data.
This study introduces an uncertainty-aware benchmarking framework that combines controlled evaluation, inter-tool agreement analysis, and expanded feature profiling to identify sequence properties and patterns driving classification ambiguity between mRNAs and lncRNAs, thereby guiding the development of more robust classifiers.
PathwaySeeker is an evidence-grounded AI system that integrates proteomic and metabolomic data to reconstruct organism-specific metabolic networks, utilizing an "Oracle-in-the-Loop" inference method to generate and verify condition-specific biological hypotheses with explicit experimental provenance.
This paper introduces UDAL, a budget-constrained active learning strategy that combines uncertainty estimation and diversity selection to significantly reduce the cost and time of wet-lab TCR--pMHC validation while achieving predictive performance comparable to models trained on much larger random datasets.