1200 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 rigorously benchmarks seven state-of-the-art computational methods for removing ambient RNA from single-cell and single-nucleus RNA sequencing data, revealing that while no single method excels across all scenarios, CellBender, DecontX, and SoupX generally demonstrate superior performance.
This paper introduces the DGIdb Model Context Protocol (MCP) server, which enables large language models to access up-to-date drug-gene interaction data from the DGIdb API via natural language, thereby significantly enhancing their ability to provide accurate biomedical answers.
This study introduces Adaptive Cluster-Count Autoencoders with Dirichlet Process Priors, which significantly enhance the geometric compactness and separation of single-cell latent spaces at a modest cost to label-recovery accuracy, thereby establishing a task-dependent trade-off where nonparametric priors are optimal for trajectory analysis and manifold visualization rather than strict cluster counting.
The paper introduces Topic-FM, a novel family of neural topic VAEs that enforces simplex constraints via a logistic-normal Dirichlet prior and employs a conditional optimal-transport flow to simultaneously enhance clustering performance, supervised discrimination, and biological interpretability of gene programs across diverse single-cell RNA sequencing datasets.
This study identifies distinct serum metabolic signatures, including specific acylcarnitines, diet-derived compounds like piperine and lutein, and altered drug metabolism, that serve as molecular predictors of exceptional cognitive resilience in a longitudinal aging cohort.
This study demonstrates that integrating subcellular localization with RNA expression data significantly improves the prediction of protein detectability across human cancers, revealing that widespread RNA-protein discordance is driven by structured biological processes rather than stochastic variation.
CROWN introduces a machine learning-ready dataset of 153,005 high-quality protein-ligand complexes that reconciles the trade-off between structural reliability and data diversity by applying a comprehensive automated pipeline with a novel energy minimization step to the PLInder database, offering a geometry-centric resource for training and benchmarking interaction prediction models.
This paper introduces Claw4Science, a curated dataset and public platform that unifies the fragmented OpenClaw scientific agent ecosystem by cataloging 91 projects and 2,230 skills to facilitate discovery, analysis, and standardized development in bioinformatics and scientific workflows.
This paper evaluates strategies for robustifying Random Forests against response contamination in genomic prediction, finding that data transformation and ranking-based approaches offer the most effective remedies for recovering latent signals in noisy breeding datasets while advising that standard models remain preferable for clean data.
The paper introduces BulkMonSTR, a computational framework that combines STR-specific error modeling with machine learning to accurately detect and distinguish genuine mosaic short tandem repeat mutations from sequencing noise and germline variants in bulk sequencing data, outperforming existing methods across diverse sample types.