Discover, rate, and discuss biology papers.

The paper evaluates learned fungal ITS embeddings against correctly configured alignment and finds alignment performs better in open-world settings due to evaluation choices.

The paper uses computational methods to evaluate how SoxB thiohydrolase binds thiosulfate in soil bacteria, finding higher binding affinity and structural stability under alkaline conditions.

COCOA-Tree reanalyzes coevolution datasets to uncover evolutionary properties of coevolving residues and shows different metrics identify distinct groups.

MetaCarto automatically generates readable genome-scale metabolic maps by organizing reactions into biologically meaningful layouts, matching curated pathway representations and producing 2621 maps for 108 BiGG models.

The paper introduces an enhanced pipeline for multi-omic integration using topological data analysis to rank genes by disease relevance, validated on TCGA breast cancer data.

HR-FRGS identifies biomarkers from cancer RNA-seq data using hypergraph learning, achieving high classification performance and biological relevance.

The paper develops a machine learning framework to predict MHC class II binding for canonical and modified peptides by incorporating chemical features, improving immunogenicity risk assessment for non-canonical peptide drugs.

StriMap models TCR-peptide-HLA interactions using structural and sequence features, achieving state-of-the-art performance and identifying disease-associated peptides in ankylosing spondylitis.

EISCA and EISTA are pipelines for single-cell and spatial transcriptomics analysis that provide standardized, modular workflows for data processing and downstream analysis.

This paper develops a language-model agent for hit-to-lead optimization that outperforms existing methods on four kinase targets in a computational benchmark.

This study evaluates a genome language model's ability to predict the effects of sequence changes in phages using experimental data, finding mixed success in distinguishing functional from nonfunctional mutations.

This study benchmarks 16S rRNA gene analysis pipelines in high-diversity datasets, revealing trade-offs between clustering accuracy, species recovery, and abundance reconstruction.

Vir2vec is a genome-wide viral embedding model that outperforms existing models in viral genome understanding tasks and provides explainability for host adaptation mutations.

Kryptix-1 improves cryptic pocket detection by combining conformational sampling with a consensus algorithm, outperforming static-structure methods and prior computational approaches on a benchmark dataset.

LiFT automates the quantification of DNA damage foci dynamics in live-cell imaging, enabling reproducible and scalable analysis of DSB kinetics in cancer research.

This study evaluates an AI agent's ability to control biological workflows versus make biological judgments, finding it excels at orchestration but performs poorly at adjudication.

This paper develops a multi-omic framework to predict human immune responses from mouse models across various stimuli, finding that functional modules are more conserved than individual genes.

The paper constructs a modular core network from Escherichia coli transcriptome datasets using a hypergraph-based pan-network approach, revealing operon-related modules and their dynamic reorganization.

TopoFlow generates protein conformational ensembles using evolutionary and structural information, improving benchmark metrics and aligning with experimental data.

The paper presents a Nextflow-based pipeline for genetic association analysis of exome sequencing data, designed to simplify and standardize the process for small cohorts.