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.
MICRON is a novel, segmentation-free, fully automated multiple-instance learning tool that leverages spatial imaging proteomics data to accurately identify outcome-associated immune microenvironments and improve prognostic and diagnostic predictions, as demonstrated by its discovery of key cell-cell communication patterns linked to survival in brain cancer.
FlyPredictome presents a comprehensive structural atlas of 1.5 million predicted protein-protein interactions in Drosophila, utilizing AlphaFold-Multimer to map binding interfaces, validate functional relevance through mutation analysis, and provide an open database for exploring the organism's interactome.
The paper introduces trackDJ, a user-friendly R package that streamlines the reproducible, programmatic generation of publication-quality epigenomic visualization figures by offering high-level plotting functions with sensible defaults to bridge the gap between interactive genome browsers and complex existing tools.
This 2026 update describes the enhancements to the DRSC Integrative Ortholog Prediction Tool (DIOPT), including expanded species and algorithm coverage, improved web portal features, and the introduction of a specialized sister tool for arthropods, all aimed at strengthening its utility for cross-species functional genomics research.
TFBindFormer is a hybrid cross-attention transformer that significantly improves the accuracy and scalability of transcription factor-DNA binding predictions by explicitly integrating DNA genomic features with TF-specific protein sequence and structural information, outperforming existing DNA-only models across diverse cell types and genomic contexts.
CROssBARv2 is a unified, scalable computational framework that integrates heterogeneous biomedical data into a provenance-rich knowledge graph with vector embeddings, enabling AI-driven exploration, hallucination-free natural language querying via CROssBAR-LLM, and advanced predictive modeling for drug repurposing and protein function prediction.
This study presents a large-scale computational screening of nearly 630,000 natural products using a novel three-tier selectivity strategy to identify 10 ultraselective Nrf2 activators that effectively bind KEAP1 while minimizing off-target interactions with PXR and CYP2D6, thereby offering a promising pathway for developing safer therapies for oxidative stress-related diseases.
This paper benchmarks various precision matrix estimation methods for differential co-expression network analysis using simulated data, revealing that performance is highly dependent on specific data characteristics and identifying GLassoElnetFast as the most accurate method while emphasizing the need for comprehensive evaluation frameworks to avoid misleading conclusions.
This study integrates sex-biased transcriptomic profiles with CRISPR screens to reveal that while sex-biased gene expression can influence sex differences in gene essentiality, sex-biased functional dependency is primarily driven by direct, expression-independent effects, particularly on the X chromosome where dosage plays a dominant role.
This study demonstrates that integrating biology-informed sequence features—specifically those capturing aggregation propensity, topology, and context-dependent signals—significantly improves the experimental success rate of de novo protein binder designs beyond traditional structure- and affinity-based evaluations.