1210 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 reveals that while the AlphaGenome model effectively predicts chromatin accessibility variation by capturing local regulatory grammar, it significantly underperforms in predicting gene expression variation due to the challenge of modeling the long-range regulatory integration required for inter-individual differences.
MLMarker is an interpretable machine learning framework that utilizes a Random Forest model trained on healthy tissues to compute continuous similarity scores and identify tissue origins in complex proteomics data, including sparse biofluid samples and metastatic tumors.
PHoNUPS is a free, open-source R-based software tool designed to standardize the analysis and visualization of multi-instrument extracellular vesicle data by unifying diverse file formats into publication-ready statistics, histograms, and contour plots to enhance reproducibility and adherence to MISEV reporting standards.
This paper introduces a hybrid quantum-classical graph neural network that integrates short-term molecular dynamics simulations and variational quantum circuits to significantly improve the accuracy and parameter efficiency of predicting protein-ligand dissociation rates for drug design.
BiomarkerKB is a comprehensive, FAIR-compliant knowledgebase and graph-based platform that harmonizes over 200,000 biomarker-disease associations from diverse sources into a standardized framework to enable reproducible exploration and discovery in precision medicine.
The authors developed EMTscore, an open-source R package that enables efficient, unbiased scoring of multiple divergent epithelial-mesenchymal transition (EMT) pathways from single-cell or bulk omics data to facilitate the rapid screening of correlations between EMT and other cellular processes.
This paper introduces Pseudotime Graph Diffusion (PGD), a lightweight post hoc framework that enhances the visualization and interpretation of single-cell trajectories by smoothing cell-level features and gene expression along inferred paths using random-walk diffusion.
RFhap is a novel trio-based phasing method that utilizes multi-k-mer markers and a random forest classifier to significantly improve haplotype-resolved de novo assembly accuracy and contiguity in human genomes compared to existing tools like Hifiasm-Trio.
The paper introduces scGeneLens, a framework that reveals how different single-cell foundation models (scFMs) like scFoundation and scGPT develop distinct "pluralistic" perceptions of cells by prioritizing either cell-type marker genes or shared biological pathways, thereby offering actionable insights for future model design.
Thyra is a modern Python library that bridges Mass Spectrometry Imaging with the SpatialData framework to overcome data fragmentation, enhance interoperability, and enable unified multi-modal analysis within the spatial omics ecosystem.