857 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.
ToxiVerse is a freely accessible, user-friendly web platform that integrates curated toxicity datasets, chemical bioprofiling, and automated machine learning tools to enable researchers without programming expertise to predict chemical toxicity and support drug development and environmental safety.
This study benchmarks deep learning-based methods for GPCR peptide design, revealing that while generative models adequately sample peptide backbones, current pipelines suffer from significant confidence overestimation and sequence memorization, highlighting an unresolved scoring problem that hinders the reliable identification of valid designs.
This paper presents an extension of the SpatialLeiden algorithm that enables scalable, atlas-scale clustering of multi-sample, 3D, and multimodal spatial omics data through flexible neighbor-graph multiplexing, delivering superior modularity and stability on standard hardware while integrating with the scverse ecosystem.
HMM-Flagger is a reference-free tool that utilizes a hidden Markov model to detect structural errors in haplotype-resolved genome assemblies, demonstrating high accuracy on synthetic data and successfully identifying misassemblies and validating novel configurations in human genome projects like HG002 and the Human Pangenome Reference Consortium.
The paper introduces TreeMS2, a scalable and annotation-independent computational framework that constructs molecular phylogenies directly from raw mass spectrometry data to reveal evolutionary relationships and functional adaptations across proteomic and metabolomic phenotypes.
The paper introduces STEQ, a fast and statistically consistent distance-based method for estimating large-scale species trees from gene trees that achieves competitive accuracy with leading methods like ASTRAL while offering superior scalability.
This paper presents a rapid, label-free, and non-destructive method for quantifying DNA fragment length distributions by integrating ATR-FTIR and Raman spectroscopy with deep learning models, achieving high accuracy and successful deconvolution of complex mixtures with minimal sample requirements.
This paper introduces APEX, an explainable AI framework that integrates evolutionary embeddings and graph attention networks to enable cross-species pathogen target discovery and pocket-guided molecular design, successfully identifying novel drug targets and generating specific inhibitor candidates for antimicrobial development.
The paper introduces scDynOmics, a scalable and interpretable transformer model that leverages gene regulatory networks and Linformer-style attention to learn compact multimodal single-cell representations, which are then efficiently adapted via low-rank modules to achieve state-of-the-art performance in cellular classification and trajectory analysis.
This study integrates network pharmacology with experimental verification to demonstrate that Panax notoginseng accelerates skin wound healing in rats by modulating key inflammatory cytokines (TNF-α, IL-6, IL-10) through multi-component, multi-target regulation of NF-κB, MAPK, and JAK-STAT signaling pathways.