Discover, rate, and discuss biology preprints.

PandaMap generates 2D/3D protein-ligand interaction diagrams, reports, and CSV files from PDB files, using validated distance thresholds and open-source tools.

Scop3P-Toolkit provides an open-source framework for integrating PTMs, mutations, and proteomics data with structural and functional annotations to enable reproducible analysis.

FOCUS integrates spatial multi-omics data through preprocessing, alignment, and resolution-matching, producing a unified data object compatible with scverse tools.

This paper introduces scDMC, a dual-stream foundation model for single-cell data that improves representation learning and achieves state-of-the-art results in cell annotation, clustering, and integration.

This study develops a sample-specific transformer model to improve genotype imputation from sparse single-cell RNA sequencing data, enhancing the recovery of genetic cell types.

This paper introduces eCDF-copula, a method for generating synthetic longitudinal tabular data that preserves within- and between-visit dependence structures, outperforming existing methods in resemblance and utility.

SpliceImpactR identifies and predicts the impact of alternative RNA processing events on protein function across the human transcriptome, revealing widespread context-dependent effects on the proteome.

The paper introduces SEGUID v2, an updated checksum system for biological sequences, to handle circular, linear, single- and double-stranded DNA and RNA sequences.

This paper introduces a constrained optimization method for designing 5'UTR sequences using deep learning models, ensuring candidates stay within a trust region defined by an OOD score based on KNN distance.

The paper develops PhyCD to detect contamination in SARS-CoV-2 genomes, identifying 10,942 putative contamination events across nearly 5 million genomes.

This study compares different fine-tuning strategies and classical machine learning approaches for protein function prediction using benchmark datasets.

SLIM predicts single-cell genetic perturbation responses using STRING embeddings and a closed-form ridge regression model, achieving competitive accuracy with low computational cost.

The paper identifies viral carbohydrate-active genes across diverse viral lineages using a hierarchical orthology framework and reveals their evolutionary and functional diversity.

This paper introduces InterAb and InterAb-Opt, computational frameworks that predict and optimize broadly neutralizing antibodies using all-atom modeling and pre-trained language models, showing significant improvements in binding affinity and neutralization activity.

The paper introduces mindGAP, a framework to quantify individual-population gaps in depressive symptom dynamics using longitudinal data, finding divergence in symptom organization associated with depression severity and traits.

scXpand detects T-cell clonal expansion in cancer from scRNA-seq without TCR sequencing, showing accuracy across tissues and subtypes.

This study benchmarks four single-cell foundation models across four tasks, finding they do not consistently outperform traditional methods but show promise in protein expression prediction.

ASOCompass predicts ASO activity by integrating sequence, chemistry, and biological context, achieving higher correlation than baselines across diverse conditions.

LIKA, a likelihood model over many-to-many kinase-substrate networks, beats KSEA in simulations with shared substrates, recovers EGFR and ABL1 in cell lines, and ranks MAPK3 first in schizophrenia cortex.

The paper introduces a framework to assess whether in silico perturbations using foundation models reflect biological responses or confounding factors.