COVID-19

Robust Multi-Mutant Protein Stability Prediction from a Fine-Tuned Evolutionary Scale Model

Recently, high-throughput experimental techniques have propelled improvements in deep learning-based prediction of mutation effects on protein stability. However, leading stability predictors still struggle to predict the combined effect of multiple mutations and prefer mutations that negatively impact other properties, including expressibility. To mitigate these limitations, we apply Low-Rank Ada...

Symmetric Divergence and Normalized Similarity: A Unified Topological Framework for Representation Analysis

Topological Data Analysis (TDA) offers a principled, intrinsic lens for comparing neural representations. However, existing paired topological divergences (e.g., RTD) are limited by heuristic asymmetry and, more critically, unbounded scores that depend on sample size, hindering reliable cross-scenario benchmarking. To address these challenges, we develop a unified topological toolkit serving two c...

Physics in 2-Steps: Locking Motion Priors Before Visual Refinement Erases Them

Image-to-Video diffusion models leverage input images to generate visually stunning content, yet frequently produce motion that violates physical laws...

Vibe Coding Specificity Foundation Models

Molecular recognition - the determination of which agent binds which target - governs adaptive immunity, gene regulation, signal transduction, RNA sil...

Learning residue-level context for modeling protein-protein interactions

Protein language models (PLMs) enable prediction of protein properties by learning residue-level features from sequence, yet most PLM-based approaches...

An interpretable machine learning framework for dog breed inference and ancestry decomposition

The over 300 currently recognized breeds of domesticated dogs are the culmination of centuries of intense artificial selection and recurrent populatio...

Data-Efficient Exploration of Enzyme Function Using Family-Specific Machine Learning

Enzymes are essential biocatalysts across diverse industries, driving demand for high-performing variants. Foundation models are attractive for guidin...

The machine-learning classifier ALLCatchR2 identifies 20 T-ALL subtypes across cohorts and age groups

T-cell acute lymphoblastic leukemia (T-ALL) comprises molecularly diverse subtypes, but robust cross-cohort validations and operational gene-expressio...

MAGI: Mechanistic Consequences of Genetic Variants via Genomic Foundation Models

Clinical variant interpretation requires mechanism-aware evidence to guide diagnosis and clarify the biological consequences of mutations. However, ex...

Hierarchical refinements of cis-regulatory inputs improve scalable gene expression prediction

Deciphering the relationships between cis-regulatory elements (CREs) and target gene expression has long been a challenging problem in molecular biolo...

Keeping human in the loop: A three-phase generative AI workflow for research integrity in data-intensive science.A methodological case study using elite Ethiopian distance-running data

Background: Generative AI tools can support data-intensive research by writing code, drafting prose, searching analytical possibilities, and stress-te...

Design and structure of protein cages based on helical fusion and machine learning

Self-assembling protein cages are versatile nanoscale architectures with broad applications in drug delivery, vaccine development, and structural biol...

Transcriptomics-Conditioned Virtual Tissue Synthesis via Diffusion Transformers

Spatial transcriptomics couples hematoxylin and eosin (H&E) tissue morphology with spatially resolved gene expression (GE). However, generative models...

Pansoma, a machine learning tool for identifying somatic variants using pangenome graphs

Somatic variant calling, the identification of mutations in non-germline cells acquired over an individual's lifetime, is critical for studying diseas...

Mapping Genetic Risk Associations to Cellular Contexts via Deep Learning and Biological Ontologies

Translating genome-wide association studies (GWAS) signals into trait-relevant cellular contexts remains challenging due to the complexity of the geno...

Sequence-Based Prioritization of Promoter Regulatory Variants in Colorectal Cancer Using a DNA Foundation Model

Noncoding regulatory variants contribute to colorectal cancer (CRC) susceptibility, yet their functional interpretation remains difficult.This is main...

Translational bioinformatics and machine learning framework for biomarker discovery, disease prediction, and patient profiling for precision medicine

Precision medicine aims to advance our ability from a "one-size-fits-all" approach to personalized and predictive healthcare across diverse population...

Random Forest Model for Predicting Post-Lockdown Antenatal Depression Risk: A Cross-Sectional Study of Pregnant Women in China

Background As lockdown measures was eased, pregnant women faced an elevated risk of COVID-19 infection, potentially impacting their mental health. Thi...

Beyond natural amino acids: Extending immunogenicity risk assessment to non-canonical peptide drugs through chemical feature encoding

Peptide therapeutics are increasingly used to treat challenging diseases, but immunogenicity risks limit their clinical success. In silico tools enabl...

Inducible lipid storage and steatosis in the human choroid plexus associated with age and adiposity

Cells that store lipids for other cells or organs can contain ''giant'' or large lipid droplets (LLDs) greater than 2 m in diameter. In this study, hu...