Discover, rate, and discuss biology preprints.

The paper introduces the Vaccine Ontology (VO), a standardized framework for representing vaccines and related data, enhancing data integration and AI research.

rhapsodist processes BD Rhapsody scRNA-seq data using a Snakemake workflow, enabling cross-aligner comparison and generating count tables in SingleCellExperiment format.

TESSERA is a method for analyzing multi-sample spatial transcriptomics data, enabling differential expression analysis while accounting for spatial correlation and sample-specific factors.

This paper benchmarks cross-species adaptation of a human retinal model to rodent OCT for age estimation, finding that CNN baselines outperform transformer models in small-sample settings.

siMILe identifies condition-specific protein structures from SMLM data by using adversarial erasing and multiple instance learning, showing improved detection of caveolae and structural variations.

This paper provides theoretical analysis of bias in differential abundance estimation using compositional and log-ratio transformations in microbiome data.

This paper introduces a genomic framework to prioritize mouse strains for preclinical disease modeling, showing high sensitivity in predicting strain vulnerability to diseases.

linearPOA introduces a memory-efficient framework for Partial Order Alignment with linear space complexity, significantly reducing memory usage for long-read sequencing.

CHAMPOLLION improves multi-omics integration by using regularized optimal transport to align unpaired cells and reveal cross-modal relationships, outperforming existing methods with fewer paired cells.

The paper identifies druggable targets in antiphospholipid syndrome using systems pharmacology, revealing two key modules linked to interferon-I signaling and degranulation pathways.

SpatialQuery identifies multicellular spatial patterns and characterizes their molecular programs in spatial omics data, revealing cross-germ-layer signaling and disease-specific niches.

The paper examines how proteolysis in seeds is influenced by germination, homogenisation, and species mixture, finding increased proteolytic activity and peptide release.

The paper develops a deep learning pipeline using U-Net models to segment hepatic vessels and lobules in histological images, achieving high Dice scores for various structures.

EnzCast predicts enzymatic kinetics from multi-modal data, and IsoKin infers patient-specific in vivo kinetics, showing improved uncertainty reduction in colorectal cancer.

CountESS provides a flexible, graphical pipeline for DMS analysis, supporting diverse workflows and input formats to generate variant scores.

Spartan identifies spatial domains and variable genes by integrating spatial topology and activation signals in spatial transcriptomics data.

This study introduces a computational pipeline using secondary structure elements to detect protein symmetry and structural rearrangements, identifying 17,130 circularly related proteins and 26,739 proteins with indel mutations.

The paper develops a method called Newmanization to reduce tissue signals in transcriptomic data and applies it to digestive tract cancer samples, showing improved clustering and separation compared to mutation data.

ICON improves cell type classification in scRNA-seq by incorporating isoform-level data, achieving better resolution than gene-level methods.