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This study characterizes repeat architectures in protozoan parasites, identifying lineage-specific clusters with distinct complexity patterns and functional roles.

This study links germline variation to tumor evolutionary dynamics in multiple myeloma, identifying TBKBP1 as a mediator of subclonal expansion and clinical outcomes.

CiliAI automates cilia segmentation and fluorescence quantification in confocal images, showing agreement with manual methods and reducing analysis time.

This paper introduces a method to compress protein language model embeddings using polynomial trajectories and PCA, achieving efficient storage and reconstruction without loss of downstream task performance.

The paper introduces GLOF, a large-scale expert-curated dataset of missense variants classified as loss-of-function, gain-of-function, or neutral, to benchmark computational methods for predicting variant functional mechanisms.

This paper introduces KM-GPT-DCH, a method using LLMs and Bayesian analysis to evaluate and compare biomedical hypotheses by analyzing scientific literature.

The paper introduces SEED, a plant-specific deconvolution framework for bulk RNA-seq data, and applies it to identify terroir-associated cell subtypes in Cabernet Sauvignon grapes.

CREP predicts cis-regulatory element identity from DNA sequence using a fine-tuned Enformer model trained on cell-type-specific annotations.

This study evaluates how epigenetic clocks respond to fasting and refeeding, finding that some clocks show significant shifts while others remain stable, highlighting the importance of immune cell composition and study design.

The paper introduces SNAC-DB, a database with expanded structural diversity for antibody and NANOBODY VHH-antigen complexes, and benchmarks seven models showing limited accuracy and ranking issues.

GLEAM provides a web-based toolkit for accessible and rigorous machine learning analyses in biomedical research, producing accurate models across three tasks.

LinkD is a drug repurposing platform that integrates molecular, phenotypic, and clinical data to prioritize drugs, showing reduced prostate cancer incidence with beta-blockers.

The paper introduces VitaGRN, a multimodal biophysical framework that predicts perturbation responses and uncovers non-canonical regulatory mechanisms, demonstrating robust generalization across cell lines and factors.

CytoGem-XAI combines hypergraph neural networks with interpretable analysis to improve metabolic modeling and prediction in E. coli, outperforming existing methods and revealing new essential nutrients.

VelocityFM predicts short-horizon protein trajectories using flow matching in velocity space, achieving high TM-scores and geometric validity on unseen proteins.

The paper develops a machine learning framework to assess quality of chromatin accessibility bigWig tracks using biologically relevant features and internal controls.

This paper introduces a dual-channel fusion graph convolutional network to reconstruct gene regulatory networks and identify key regulators from single-cell RNA sequencing data, showing improved accuracy and robustness.

BacteReason predicts bacterial antibiotic susceptibility with mechanistic rationales, improving accuracy over baselines using reasoning supervision.

The paper develops a compound prioritization strategy using a multi-layer perceptron trained on docked poses from multiple docking approaches to identify putative binders for the nuclear receptor LRH-1.

KDM provides a unified framework for motif discovery, analysis, and binding prediction by embedding DNA/RNA sequences and motifs in a shared k-mer space using the Hellinger transformation and Bhattacharyya coefficient.