1197 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.
SPARKLE is a fast, reference-free computational method that corrects local RNA leakage in high-resolution spatial transcriptomics by leveraging within-sample out-of-mask evidence to estimate and remove ectopic signals, thereby improving cell-type localization and downstream analysis reliability.
This study introduces a novel late fusion relational graph convolutional network (RGCN) ensemble that integrates competing endogenous RNA (ceRNA) and copy number variation (CNV) data to identify five prognostic biomarkers, including hsa-miR-196a and hsa-miR-224, for improved survival prediction and therapeutic targeting in glioblastoma.
GlycoMeSH is a comprehensive resource that bridges the gap between glycan structures and biomedical context by linking them to Medical Subject Headings (MeSH) through an inference model and traceable database, thereby enabling systematic enrichment analysis across glycoscience datasets.
PandaDock is an open-source molecular docking platform that integrates flexible-ligand conformational search with analytic gradients, specialized modules for complex binding scenarios, and a scalable SE(3)-equivariant neural network scoring function to achieve competitive pose recovery and affinity prediction performance across diverse protein-ligand targets.
The paper introduces Mantpy, a spatial proteomics framework that represents the extracellular matrix and its interface with cells as spatial graphs to enable joint analysis, visualization, and machine learning beyond traditional cell-centric approaches.
PerturbLDM is a conditional latent diffusion framework pretrained on the Tahoe-100M dataset that outperforms existing methods in accurately predicting and generating single-cell transcriptional responses to unseen drug, dose, and cell line combinations across diverse biological contexts.
This paper demonstrates that applying conformal prediction to the BayesAge epigenetic clock enables the generation of distribution-free, calibrated prediction intervals for DNA methylation-based age estimation using a minimal set of CpG sites, thereby addressing the lack of uncertainty quantification in existing point-estimate models.
This paper demonstrates that cryptic binding site detection is currently limited not by the ability to propose candidates, but by poor ranking and lack of consensus, revealing that separating coverage from conversion metrics uncovers significant performance gaps and shows that combining geometric and language-model-based detectors within a strict candidate budget yields the most practical improvements.
The authors introduce ProtInt, a deep learning framework that successfully integrates proteomic data from cancer cell lines and patient tumors by overcoming missing value challenges, thereby outperforming existing methods and revealing key biological shifts needed to better align preclinical models with clinical reality.
This systematic benchmarking study reveals that the choice of computational method, rather than underlying biology, is the primary determinant of in silico perturbation results, as most widely used approaches fail to detect causal transcription factor-to-pathway signals and often produce conclusions that contradict experimental CRISPRi validation.