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.
TriGraphQA is a novel triple graph learning framework that improves protein complex quality assessment by explicitly decoupling intra-chain folding and inter-chain binding through three geometric views and an interface context aggregation module, thereby outperforming state-of-the-art methods in identifying near-native assemblies.
RNASTOP is a novel deep learning framework that significantly improves the accuracy of mRNA chemical stability prediction and enables rational sequence optimization, thereby accelerating the development of stable and effective mRNA therapeutics.
This study utilizes computational molecular docking and immunoinformatics to identify PfCyRPA, PfMSP10, and PfCSP as top *Plasmodium falciparum* antigen candidates for malaria vaccine design by evaluating their interactions with human T-cell receptors.
This study utilizes multi-dataset transcriptomic analysis and machine learning to identify robust molecular signatures, specifically highlighting vitamin D metabolism and inflammatory pathways, as core mechanisms and diagnostic biomarkers for frailty.
This paper introduces differentiable GSEA (dGSEA), a scalable and numerically stable surrogate for classical Gene Set Enrichment Analysis that enables pathway-level supervision in transcriptomic prediction models by replacing discrete ranking operations with differentiable approximations, thereby bridging the gap between gene-wise training objectives and pathway-level interpretation.
The paper introduces ECHO, a user-friendly, Snakemake-based pipeline that leverages Oxford Nanopore sequencing to enable comprehensive, reproducible, and scalable end-to-end (epi)genetic profiling of the human repeatome.
The paper introduces scCChain, a transformer-based framework that maps spatial cell-cell communication by constructing and scoring tailored chains of cells to identify interpretable communication programs and localize interaction hotspots at both spot and single-cell resolutions.
This study systematically benchmarks five machine learning-based variant annotation methods across UK Biobank data, revealing that CADD v1.6 achieves the best signal separation while AlphaMissense shows calibration issues, ultimately providing practical guidance for method selection and a new framework for calibration assessment in rare variant association testing.
The paper introduces ISdetector, a scalable and robust bioinformatics pipeline that outperforms existing tools in precisely mapping insertion sequences and detecting associated structural variations in bacterial and archaeal genomes using short-read sequencing data.
This paper demonstrates that applying temperature scaling and epistemic neural networks significantly improves the reliability of transformer-based genomic language models when facing distribution shifts, such as noisy data or novel variants, across diverse genomic prediction tasks.