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 CoMPLip, a training-free method that enhances AlphaFold 3's accuracy in predicting membrane protein structures by explicitly co-folding them with lipid molecules to simulate a realistic membrane environment.
This paper introduces Deconomix, a comprehensive Python package and graphical user interface that enables the deconvolution of bulk transcriptomics data to infer cellular compositions, optimize gene weights for resolving small or related cell populations, account for background contributions, and estimate cell-type-specific gene regulation.
This paper presents a machine learning model that successfully learns the principles of non-bonded interactions to predict drug-receptor binding for novel chemical matter, offering a theoretical framework to overcome the limitations of current serendipitous drug discovery methods.
FlashDeconv is a scalable, structure-preserving deconvolution method that enables accurate, atlas-scale spatial analysis of Visium HD data at multiple resolutions, revealing critical biological insights such as tissue-specific resolution horizons and previously undetectable immune microdomains that are missed by current classification-based approaches.
Col-Ovo is a smartphone-based artificial intelligence tool that enables rapid, automated counting of *Aedes aegypti* eggs from field images with over 95% accuracy, effectively overcoming the manual processing bottleneck in large-scale mosquito surveillance programs.
The paper introduces PACMON, a scalable Bayesian latent factor model that integrates multi-omics data from large-scale perturbation screens to interpret gene function by jointly inferring pathway-level programs and their specific modulation by experimental perturbations.
TCRseek is a scalable, two-stage retrieval framework that combines biologically informed windowed k-mer embeddings with approximate nearest neighbor indexing and exact reranking to enable efficient, high-sensitivity search of large T-cell receptor repertoires, achieving significant speedups over brute-force methods while maintaining near-optimal accuracy.
This study demonstrates that relying on a single SNP annotation tool or gene model leads to significant discrepancies and incomplete pathway inference, whereas a multi-tool, multi-model strategy maximizes coverage and ensures robust, reproducible genomic interpretation.
dreampy is a Python implementation of the R/Bioconductor dreamlet framework that enables pseudobulk mixed-model differential expression analysis for single-cell RNA-seq data by integrating voom precision-weighted linear mixed models with empirical Bayes moderation within the scverse ecosystem.
The paper introduces Micro16S, a deep learning framework that generates phylogenetically informed, region-invariant 16S rRNA embeddings to improve microbiome representation, though its current performance on classification tasks remains inferior to classical machine learning baselines due to challenges like class imbalance.