857 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 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.
REMAG is a novel tool that leverages contrastive learning and genomic foundation models to overcome existing limitations in recovering high-quality, near-complete eukaryotic metagenome-assembled genomes from long-read metagenomic data.
To address the limitations of existing tools in flexibility and interactivity, the authors developed geneSTRUCTURE, a modern command-line and web-based platform that enables customizable, high-resolution visualization of gene structures and supplemental annotations using GFF3 and GTF formats.
This study presents the first telomere-to-telomere haplotype-resolved assembly of the autotetraploid *Dendrobium officinale* genome, revealing its recent polyploidization history and identifying root-specific SWEET genes that likely underpin its specialized mycorrhizal symbiosis.
This study proposes a machine and deep learning pipeline that classifies individuals based on genotype data to identify phenotype-associated genes, demonstrating that SNPs selected by high-performing models effectively prioritize disease-related genes with a mean gene identification ratio of 0.84.
This study employs an *in silico* multi-target approach, including molecular docking, dynamics simulations, and binding free energy calculations, to demonstrate that Withaferin A exhibits promising antiviral potential against key Marburg virus proteins (VP35 and NP) with favorable drug-like properties, warranting further experimental validation.
This study introduces a novel, open-source simulation framework to generate realistic Oxford Nanopore long-read datasets and presents the first comprehensive benchmark comparing three circRNA detection tools (CIRI-long, IsoCIRC, and circNICK-Irs), revealing their distinct performance profiles and limited overlap to guide future tool selection and development.
This paper proposes SI-ADMM, a novel joint learning framework based on coupled tensor-tensor factorization that integrates multi-view auxiliary drug information to simultaneously predict effective drug combinations and drug-drug interactions, demonstrating superior robustness and performance in both standard completion and realistic new-drug prediction scenarios.
The paper introduces t2pmhc, a structure-informed graph neural network framework that leverages predicted 3D structures of TCR-pMHC complexes to achieve superior generalization to unseen peptides and biologically consistent binding predictions compared to existing sequence-based methods.
The paper introduces RigidSSL, a rigidity-aware self-supervised learning framework that leverages geometric pretraining on static structures and molecular dynamics trajectories to significantly enhance protein designability, novelty, and the modeling of biophysically realistic conformational ensembles.