Discover, rate, and discuss biology preprints.

MetaClaw is an auditable AI agent that performs end-to-end metagenomic and multi-omics analysis, recovering key biological markers across multiple studies.

This paper introduces an alignment-last approach to transcriptomic analysis, enabling rapid biomarker discovery by using reads directly and revealing new transposable-element signatures and prognostic reads in cancer.

The paper introduces FibrilNet, a network framework that identifies conserved and tissue-specific molecular environments across systemic amyloidoses by integrating proteomic data with semantic and interaction information.

The paper maps human metabolite-protein interactions using LIGMAP and shows improved pathway coverage and prediction when combined with STRING and BioLiP2.

The paper introduces a Bayesian beta-binomial mixed-effects model to analyze zero- and one-inflated patient-reported pain scores, showing improved fit and accuracy compared to existing methods.

The paper identifies circadian-organized non-covalent PAR-binding E3 ligases in the brain, linking PAR signaling to time-dependent ubiquitin control and disease-relevant biology.

Hi-cGAN predicts Hi-C interaction matrices using conditional generative adversarial networks, achieving better performance than existing methods at certain resolutions.

Atlantis integrates structural and functional residue-level data for the human proteome, enabling complex queries and analysis of protein interactions and mutations.

The paper evaluates machine learning predictors of enzyme turnover numbers (kcat) by testing their impact on downstream metabolic models, finding that accuracy varies significantly and that validation must consider system-level effects.

PocketScope retrieves binding sites across the AlphaFold human proteome using protein language model representations and achieves top performance on a benchmark.

BioIMA is a desktop tool that automates phenotypic trait extraction from biological images, achieving high accuracy and speed compared to manual methods.

The paper annotates neuropeptides and GPCRs in Gryllus bimaculatus and predicts ligand-receptor pairs using AlphaFold3 and Boltz-2, providing a resource for studying insect endocrinology.

scRep is a foundation model for single-cell representation learning that uses self-distillation in latent space, achieving strong performance without task-specific fine-tuning.

The paper develops and validates a prognostic model using morphologic intratumoral heterogeneity from histopathology slides to predict survival in primary central nervous system lymphoma.

The paper introduces Bravais Lattice Sampling (BLS), a method for detecting connected high-density regions in 3D space, achieving high accuracy with reduced computational cost compared to traditional methods.

BC-SELECT predicts treatment response in non-metastatic breast cancer using genetic interactions and gene expression data, showing significant accuracy for PARP inhibitors and immunotherapy.

The paper introduces IFACE, a method to compute geometric-chemical distances between protein surfaces and provides surface maps, showing improved performance over existing methods in classification and clustering.

GLORB improves differential expression analysis by accounting for global expression shifts, outperforming existing methods in scenarios with widespread upregulation or downregulation.

AnnFlux models single-cell perturbation dynamics using object-conditioned neural SDEs, predicting responses to unseen perturbations and combinations.

Ageas infers cell fate from static single-cell and spatial multi-omics data without time-resolved measurements, revealing spatial fate gradients in human embryos.