Discover, rate, and discuss biology preprints.

This paper introduces scRitmo, a method to infer circadian phases and quantify biological desynchrony from single-cell transcriptomes, showing improved performance over existing methods.

The paper benchmarks protein language models for EC number prediction, finding MLP classifiers outperform BLAST and PLMs show significant gains for distant eukaryotes.

This paper develops a dynamic multimodal model for survival prediction in multiple myeloma using gene expression, lab data, and treatment history, achieving higher accuracy than existing methods.

The paper develops a machine learning model to predict protein detectability from RNA expression and subcellular localization across cancers, finding significant improvements when incorporating localization data.

This study benchmarks somatic mutation detection using the human pangenome reference and finds improved accuracy, especially in East Asian individuals.

STAPLE automates spatial transcriptomics analysis by integrating multiple tools into a modular framework and provides AI-driven interpretation of results.

The paper introduces SPLASH, a reference-free algorithm for detecting sequence variation from RNA-seq data, and applies it to discover novel regulatory mechanisms in plant transcriptomes.

The paper introduces cosmoDA, a differential abundance testing method for compositional sequencing data that reduces false positives by modeling feature interactions and covariates.

PopGenAgent streamlines population genetics analyses by organizing tasks into a structured workflow, enabling reproducible and transparent multi-step analyses with preserved intermediate results.

The paper introduces a fast, lightweight model to predict protein flexibility using atom coordinates, achieving high accuracy without requiring MD simulations.

This study evaluates protein embeddings' reliability by introducing a model-agnostic scoring framework to quantify their ability to capture biologically relevant information.

emb2dis predicts protein disorder using a deep learning model combining ResNets and dilated convolutions, achieving top performance on benchmark datasets.

BulkMonSTR detects mosaic STR mutations in bulk sequencing data with improved precision and F1 scores compared to existing methods.

OligoN-design designs taxon-specific oligonucleotides from target and non-target sequences, outperforming existing tools by handling large datasets efficiently.

The paper develops a transcriptome-based method to match bulk RNA-seq data from kidney cell lines to single-cell reference datasets, identifying accurate cell type identities and evaluating method performance.

The paper introduces a curated dataset and platform for the OpenClaw scientific agent ecosystem, analyzing its structure and challenges in reproducibility and governance.

MIMIC is a pipeline that integrates Mass Spectrometry Imaging and Imaging Mass Cytometry to analyze spatial omics data, recovering known molecular associations and revealing novel spatial relationships.

The paper identifies serum metabolic signatures associated with cognitive resilience in a longitudinal aging cohort using untargeted LC-MS/MS profiling.

The paper introduces bioIB, a framework using the Information Bottleneck method to extract compressed, signal-informative representations of scRNA-seq data, demonstrating its utility in diverse biological contexts.

The paper introduces a temporal AI model, MaxToki, trained on extensive gene data to predict cell state changes across human aging and identifies age-modulating targets.