Discover, rate, and discuss biology preprints.

Luxar compresses volumetric microscopy data into compact archives for interactive web visualization without requiring specialized software.

AF3 Inspector provides an interactive web tool to visualize and analyze all AlphaFold 3 structural models, addressing limitations in the official service.

YieldLearn estimates yield-related traits in durum wheat using physiological indicators and machine learning, showing moderate accuracy and feature importance.

DS5 is an open-source framework that standardizes HTS data storage, analysis, and drug prioritization, validated against public benchmarks and applied in clinical settings.

NexuST is a hierarchical foundation model for spatial transcriptomics that improves cell-type annotation and spatial prediction by integrating gene-level and cell-level modeling.

This study evaluates genomic foundation models for viral sequence classification, finding that NTv3-650M achieves high accuracy comparable to BLAST on standard-length sequences.

CryoMV generates and validates continuous protein conformations from cryo-EM data using structure priors and real-particle validation, achieving high resolution and robustness on benchmark datasets.

UniWave-2 improves nucleic acid sequence analysis by integrating hydrophobicity, enhancing waveform resolution with Fourier and wavelet transforms, and using a lightweight GRU model for efficient feature extraction.

DSSNA v2026 analyzes nucleic acid structures in MD simulations, identifying base pairs, hydrogen bonds, and stacking interactions, showing that canonical pairs are stable while non-canonical ones are transient.

This study benchmarks single-cell foundation models for aging biology, finding that Geneformer excels in age prediction and pseudotime concordance, while SCimilarity outperforms others in rare state identification.

The paper shows targeted finetuning can enable co-folding models to learn ligand-induced conformational states not present in training data.

SpiderNet identifies cell-cell communication programs from spatial transcriptomics data, revealing key interactions and their associations with phenotypes and therapeutic responses.

CheckAMG identifies auxiliary viral genes by combining annotation-based prediction with a genome-language model, improving accuracy and uncovering novel genes with no sequence similarity to references.

PTMExplorer is a visualization platform that integrates PTM data with 3D structure, functional annotations, and disease associations to identify biologically significant modification sites.

HANAMI improves drug-gene-disease motif prediction by integrating heterogeneous biomedical data and using graph contrastive learning, achieving up to 6% better performance than existing methods.

RosaSeed improves short read alignment speed while maintaining accuracy comparable to BWA-MEM2 by using a configurable seeding strategy.

Pop-Corn predicts how genetic perturbations reshape cell-type composition in single-cell and spatial contexts without reconstructing gene expression.

This study identifies age- and disease-specific remodeling of cardiac lymphatic endothelial cells through single-nucleus transcriptomics and spatial analysis.

sfate computes annotation-targeted absorption probabilities by column-wise GMRES on a latent kNN graph, skipping Schur decomposition; a 74,984-cell mouse microglia analysis peaked at 1.76 GiB.

This paper introduces SurvMOCA-GNN, a multi-omics integration framework that improves patient stratification in AML by incorporating biological networks and clinical data.