Discover, rate, and discuss biology papers.

SCiMS predicts host chromosomal sex from metagenomic data, outperforming existing tools at low host coverage and across species.

BactMentha is a database of annotated bacteria-host protein interactions for human, mouse, and rat, including virulence proteins and predicted interaction interfaces.

DCSE predicts drug combination side effects using a deep learning model, outperforming existing methods in realistic settings with limited data.

The paper introduces c-UMAP, a cell-driven information fusion framework for single-cell RNA-seq data that improves clustering accuracy and interpretability over traditional methods.

This paper shows DE-SWAN is unreliable for studying aging rate changes due to sensitivity to age distribution and model parameters.

This study evaluates zero shot, one shot, and few shot prompting regimes for transformer-based synthetic health tabular data generation using small models, finding zero shot improves fidelity but reduces privacy.

PowerFit improves computational performance for rigid-body fitting of atomic structures into cryo-EM maps while maintaining accuracy and providing interactive visualization.

This paper investigates whether histology can improve transcriptomic foundation models by providing cross-modal supervision, but finds limited benefits due to slide-specific structure in gene-expression embeddings.

This paper introduces scTAPAS, a method to reconstruct donor genotypes from scRNA-seq data, enabling eQTL and HLA-TCR analysis without conventional genotyping.

PRIZM uses low-N data and zero-shot models to rank protein variants, improving design efficiency with minimal experimental data.

trFlow generates all-atom protein conformations faster and more accurately than existing methods by using flow matching with three key innovations.

CellART extracts single-cell information from high-resolution spatial transcriptomics data across multiple platforms, demonstrating efficiency and accuracy in cancer datasets.

Kidzoi is a kidney-specific sequence-to-function model that improves variant effect prediction and chromatin accessibility prediction in proximal tubule cells.

GalaxyMemMD provides a reproducible, high-throughput pipeline for membrane protein simulation and analysis, integrating embedding, MD simulation, and downstream analysis.

The paper introduces LTDA, a post-embedding module for PPI type prediction, showing improvements over existing methods on benchmark datasets.

This paper examines the limits of Bayesian divergence time estimates in microbial evolution, finding that uncertainty scales with distance to calibration points rather than absolute age.

AmyloDeep predicts amyloid propensity from amino acid sequences using an ensemble of deep learning models, achieving high accuracy and robustness.

WrapEvoFS selects a representative feature set from stochastic searches with regret constraints, ensuring auditable and reproducible results without claiming predictive superiority.

The paper introduces CLARA, a hierarchical learning framework to mitigate class imbalance in graph data, achieving high F1-score in protein binding site prediction.

This paper introduces a biology-guided machine learning method for automated anatomical landmark transfer across diverse biological datasets, showing significant improvements in accuracy and robustness under severe perturbations.