Discover, rate, and discuss biology preprints.

The paper identifies abiotic stress-responsive tncRNAs in rice and chickpea, showing their potential role in plant stress adaptation.

DLDN-Bench benchmarks deep learning de novo peptide sequencing tools using human mass spectrometry data, showing performance improvements over traditional methods.

The paper introduces an interactive spatial proteomics workflow for analyzing imaging data, enabling reproducible and customizable analysis of protein expression and localization in tissues.

This study benchmarks 20 cell type annotation methods on spatial transcriptomics data to evaluate their performance in resolving cell types, subtypes, and biological organization across diverse datasets.

HyperDev uses hyperbolic geometry to classify C. elegans developmental stages with few labeled examples, achieving high accuracy and biological alignment.

This paper introduces DFVB, a method that reduces computational costs of DFBA by using sparse flux vectors, achieving faster simulations and better pathway identification.

PhaseWY is a pipeline that identifies sex-chromosomal regions and extracts Y/W sequences from whole-genome sequencing data, demonstrating its utility in non-model organisms.

The paper introduces ResistAgent, a framework for resistance-aware lead optimization that converts mutational liabilities into design objectives, improving robustness over average-affinity behavior.

Drug-Prot quantifies drug effects and interactions using proteomic data, revealing protein-level causal relationships and actionable drug combinations.

This study introduces GARN3, an improved RNA 3D structure prediction technique that enhances visualization with additional pseudoatoms and a machine learning model, showing better performance on large molecules.

ATLAS integrates single-cell RNA-seq and ATAC-seq data to improve trajectory inference by jointly analyzing transcriptomic and chromatin accessibility information.

CellTosg2Sequence integrates biomedical text and signaling graphs into a language model for single-cell analysis, improving performance over existing models.

The paper evaluates deconvolution methods' robustness to incomplete reference matrices and introduces DICEPro to improve performance by adjusting reference signatures.

This study introduces a complex-valued representation for time-series gene expression data, showing it captures distinct gene-gene similarities and temporal programs comparable to conventional methods.

The paper presents an AI-based pipeline for predicting bevacizumab response in ovarian cancer using digital pathology images, achieving improved AUC performance compared to existing models.

ReSeT selects reference genomes by optimizing taxonomic discrimination and representativeness, improving profiling accuracy in high-ambiguity datasets.

The paper introduces Suco and Compocyte to classify immune cells from 3,965 patients, identifying new macrophage and monocyte states and T cell memory erosion in metastases.

The paper introduces πDIA-CLIP, a zero-shot framework for DIA proteomics data analysis that improves protein identification and reduces false positives across diverse datasets.

Nevermore improves lead optimization by balancing binding affinity and developability constraints using a database-grounded framework with interpretable molecular edits.

Accelign accelerates pairwise sequence alignment using GPUs, achieving significant speedups over existing CPU and GPU implementations for DNA, RNA, and protein sequences.