1012 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 introduces a deep-learning model that successfully translates individualized brain topography maps into functional connectomes, thereby establishing a direct link between spatial organization and network connectivity to unify diverse rsfMRI research perspectives.
The paper introduces an updated version of the TRACEY resource that significantly improves SNARE protein domain annotation by incorporating a vastly expanded, non-redundant dataset of nearly 19,000 curated sequences to generate enhanced HMM profiles and a redesigned web interface for more accurate detection of divergent and lineage-specific paralogs.
This study demonstrates that acute fasting induces small, systematic shifts in specific epigenetic clocks—particularly PC-based and SystemsAge variants—driven by immune cell redistribution, whereas mortality-trained clocks remain stable, highlighting that clock reliability is context-dependent and requires perturbation-specific assessment beyond standard ICC metrics.
The paper introduces CLASPP, a unified model for predicting diverse post-translational modifications that overcomes data imbalance challenges through contrastive learning, hierarchical data curation, and a multi-stage training strategy to improve prediction accuracy across various organisms.
BacteReason is a fine-tuned large language model that predicts bacterial antibiotic susceptibility and provides mechanistic rationales by leveraging a knowledge-grounded teacher model, resulting in significantly improved prediction accuracy over baseline approaches.
The paper introduces GLOF, a large-scale, expert-curated benchmark dataset comprising over 112,000 missense variants across nearly 3,000 human genes, which classifies variants as gain-of-function, loss-of-function, or neutral to facilitate the development and evaluation of computational methods for predicting variant mechanisms.
VelocityFM is a novel short-horizon protein trajectory predictor that utilizes rectified flow matching in velocity space to generate geometrically valid, clash-free, and fold-preserving dynamic conformations for unseen proteins, achieving high structural accuracy with a median TM-score of 0.929.
The paper introduces GLEAM, a web-based, code-free software toolkit built on the Galaxy workbench that enables biomedical researchers to perform rigorous, reproducible, and accessible supervised machine learning analyses across diverse data types while adhering to best practices.
This paper presents a scalable, GPU-accelerated pangenome construction method that leverages protein language model embeddings and advanced clustering to outperform existing tools like SCARAP in generating functionally coherent and specific protein clusters, particularly on experimentally validated datasets.
Ontologizer 3 is a freely available, cross-platform desktop application that facilitates Gene Ontology enrichment analysis by offering both frequentist and Bayesian methods, with the latter demonstrating superior precision in identifying causal terms by accounting for hierarchical gene set overlaps.