1209 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 demonstrates that an ensemble-based genomic prediction approach (EasiGP) significantly improves the accuracy of predicting maize flowering-time traits by leveraging the complementary strengths and diverse views of multiple individual models to offset prediction errors and better capture underlying genetic variation.
ChatSpatial is a Model Context Protocol-based platform that leverages schema-enforced agentic orchestration to unify over 60 spatial transcriptomics methods across Python and R ecosystems, enabling reproducible, cross-platform, and context-aware conversational workflows that eliminate the need for manual code generation.
This study systematically benchmarks deconvolution methods for plasma cell-free RNA, revealing that while tissue-of-origin inference is robust across simulated and clinical datasets, cell-type-of-origin inference remains highly variable and sensitive to methodological and reference choices.
The paper introduces the Fractal ecosystem, comprising a standardized task specification and a scalable platform, to enable reproducible, modular, and FAIR-compliant bioimage analysis workflows natively processing large-scale OME-Zarr microscopy data.
This study benchmarks the performance of 29 machine learning algorithms, 80 deep learning models, and 3 polygenic risk score tools across 80 binary phenotypes from the openSNP dataset, revealing that machine learning approaches outperformed traditional tools for 44 phenotypes while polygenic risk scores were superior for the remaining 36.
The paper introduces anndataR, an R package that enables seamless interoperability between R and Python in single-cell transcriptomics by allowing native reading and writing of H5AD files, conversion to and from SingleCellExperiment or Seurat objects, and rigorous testing to ensure long-term compatibility between the two languages.
This paper introduces a standardized, nearly doubled dataset of mammal-infecting viral pathogens with refined host labels to demonstrate that machine learning models achieve significantly better predictive performance for broader taxonomic ranks and when training and test sets share closer phylogenetic relationships, while highlighting the current limitations of generalizing these models to completely novel viral families.
MiRformer is a unified generative framework that leverages a dual-transformer architecture with sliding-window attention to accurately predict miRNA-mRNA interactions, localize binding sites, and synthesize biologically plausible, target-specific miRNA sequences directly from raw long mRNA sequences.
The Stochastic System Identification Toolkit (SSIT) is a fast, flexible, open-source MATLAB package designed to model, simulate, and fit stochastic biochemical systems using diverse computational methods, enabling robust parameter inference, noise handling, and optimal experimental design for single-cell and other count-based data.
The paper introduces SPAE, an integrated Sinusoidal and Piecewise AutoEncoder model that significantly improves the accuracy and robustness of characterizing cell cycle dynamics and states in single-cell RNA-seq data compared to existing methods.