856 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.
The paper introduces GRASP, a parameter-efficient framework that utilizes gene- and relation-specific soft prompts to enhance the scalability and generalizability of large language models for inferring diverse gene network interactions.
This study identifies ABCA3 and SCN4B as exercise-related biomarkers for Stanford type A aortic dissection, demonstrating their diagnostic potential through a nomogram while elucidating their involvement in circadian rhythm and immune regulation pathways and suggesting zonisamide and MRS1097 as possible therapeutic agents.
This study identifies five novel ZINC compounds (Z1–Z5) as potential antivirulence inhibitors against the *M. tuberculosis* EccA1 enzyme through virtual screening, docking, and molecular dynamics simulations, demonstrating their superior binding affinity compared to known inhibitors and favorable drug-like properties.
MAJEC is a unified, fast Expectation-Maximization framework that accurately quantifies genes, isoforms, and individual transposable element loci from RNA-seq data by leveraging splice junction evidence to resolve read overlaps, thereby significantly reducing false attribution artifacts compared to existing tools like TEtranscripts and Telescope.
This paper presents a hierarchy-aware gene exploration platform that integrates HGNC biological knowledge into a hyperdiffusion-based similarity kernel to significantly enhance the interpretability and functional coherence of transcriptomic analysis, as demonstrated by its successful application in identifying key toxicological modules in acetaminophen-induced hepatotoxicity.
This study systematically benchmarks single-cell drug response models across diverse datasets, revealing that while scDEAL demonstrates superior robustness to class imbalance, most current methods struggle to predict intrinsic pre-treatment resistance despite effectively capturing post-treatment transcriptional changes, thereby highlighting the need for next-generation models with greater clinical relevance.
This paper introduces **found**, a flexible Python and R implementation of the HiDDEN method that enables robust inference of cell-level perturbations from structured batch-level label noise in single-cell data through customizable pipelines and systematic benchmarking of modeling choices.
This paper introduces CloneMLP-NPE, a likelihood-free, simulation-based framework utilizing neural posterior estimation and normalizing flows to accurately infer intra-tumor selection coefficients from single-cell CNA genotypes, outperforming both set-based and consensus-based baseline approaches.
This paper presents BioClaw, a human-bot collaboration ecosystem that transforms group chats into persistent, secure workspaces by converting natural language requests into automated, tool-grounded biomedical analyses executed within isolated Docker containers.
GraphMana introduces a graph-native data management system for population genomics that replaces fragmented file-based workflows with a persistent database to enable incremental sample addition, provenance tracking, and efficient multi-format export, as demonstrated by its ability to complete a complex 46-operation lifecycle for the 1000 Genomes Project in under two hours.