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Comp2GPR improves gene-protein-reaction rules by integrating curated protein complex data with sequence-based validation, enhancing metabolic model accuracy.

SPEAK identifies spatial domains in spatial transcriptomics data using a large language model and expert-aligned prompts, showing improved accuracy and interpretability.

This paper develops GepiClock, an epigenetic clock for cancer cells, revealing age-related therapeutic vulnerabilities in cancer cell lines.

SimMapNet improves gene regulatory network inference by integrating Gene Ontology similarities, outperforming existing methods on bacterial and fly datasets.

ABComp is a Snakemake-based pipeline for comparative genomics of bacterial clinical isolates, identifying phenotype-linked genomic markers.

This paper introduces a probabilistic preferential learning model for variant effect prediction, showing that uncertainty-aware strategies improve acquisition performance on single-mutation datasets.

KozakExplorer quantitatively analyzes translation initiation signals across genomes, revealing evolutionary patterns in eukaryotes and prokaryotes.

This paper introduces PertOmni, a multimodal framework that aligns transcriptomic data with text and image embeddings to improve perturbation effect prediction.

The paper develops LongAIRR, a computational framework for spatially resolving adaptive immune receptor repertoires from long-read sequencing, improving consensus accuracy and enabling platform-agnostic analysis.

The paper fits the Gompertz curve to PDB growth data to estimate future PDB entries and protein folds, revealing a deceleration in growth.

The paper develops deep-learning models to predict glycan-protein binding strengths using hybrid datasets and tail-aware losses, finding that glycan interactions are harder than small-molecule interactions due to chiral feature limitations.

CoLa-VAE improves denoising and representation learning in single-cell data by integrating cell-cell communication information, enhancing downstream analyses like differential communication detection and spatial deconvolution.

ARCADIA integrates scRNA-seq and spatial proteomics to reveal spatially dependent transcriptional programs without cell barcode pairing or feature-level correspondence.

FoldARE improves RNA secondary structure prediction by using pseudo-SHAPE constraints from structural ensembles, achieving high accuracy across benchmark datasets.

Nerpa 2 links biosynthetic gene clusters to nonribosomal peptides using probabilistic models, improving accuracy and scalability in pathway reconstruction.

This study analyzes a diverse glioma cohort to show ancestry influences on survival, somatic alterations, and mutational processes in diffuse gliomas.

BGC-QDR is a quantum-assisted pipeline for discovering and ranking biosynthetic gene clusters from eDNA, achieving 0.789 accuracy and 0.835 ROC-AUC using a variational quantum classifier.

CREPAS is a Nextflow pipeline for analyzing nascent chromatin sequencing data, improving scalability, reproducibility, and compatibility across multiple chromatin assays.

The paper introduces DSEN, a tissue-aware regression method that improves drug response prediction by decomposing models into shared and lineage-specific components, achieving better performance and interpretability.

The paper introduces an ambiguity-aware framework for spatial cell-type annotation, reducing cluster and cell-level ambiguity in cholangiocarcinoma data.