Genetics

Clinical trials in depression: Integrated collection across EU and US registries

Depression affects millions worldwide with both pharmacological and psychological therapies widely applied, both with limited treatment success. Many clinical trials have been undertaken to test and compare treatments for depression. In collaboration with Medicines Discovery Catapult (MDC), we developed a comprehensive database of depression-related clinical trials, including information on the av...

Multimodal AI for Precision Preventive Cardiology

Coronary artery disease (CAD) is the leading cause of death worldwide, yet it is highly preventable. Early detection is critical, particularly because the first clinical manifestation of CAD is a heart attack in ∼50% of individuals. Current clinical risk scores rely largely on traditional biomarkers and do not leverage recent advances in medical imaging and genetics. To address this, we developed ...

KG2ML: Integrating Knowledge Graphs and Positive Unlabeled Learning for Identifying Disease-Associated Genes

Biomedical knowledge graphs (KGs), such as the Data Distillery Knowledge Graph (DDKG), capture known relationships among entities (e.g., genes, diseas...

Machine learning to phenotype pain and predict response to pain interventions among young adults with irritable bowel syndrome

Irritable bowel syndrome (IBS) is a prevalent disorder whose most debilitating symptom is pain. The complex, multifactorial nature of IBS pain leads t...

A machine-learning framework to characterize functional disease architectures and prioritize disease variants

Modeling disease effect sizes from genome-wide association studies (GWAS) is critical for both advancing our understanding of the functional architect...

Identification and validation of tolerogenic dendritic cells-related biomarkers in diabetic retinopathy

Diabetic retinopathy (DR) is a primary microvascular complication of diabetes. Its pathogenesis is associated with chronic inflammation and immune res...

DNA-Based Deep Learning and Association Studies for Drug Response Prediction in Leiomyosarcoma

Leiomyosarcoma (LMS) is a rare and aggressive soft tissue sarcoma with limited treatment options and poor prognosis. Standard therapies, including dox...

Artificial Intelligence Reveals Prognostic TP53 Pathway Alterations in FOLFOX-Treated Early-Onset Colorectal Cancer Among Populations at Risk

The incidence of early-onset colorectal cancer (EOCRC; <50 years) continues to rise, with the most rapid increases observed among Hispanic/Latino (H/L...

Machine Learning for Predicting and Maximizing the Response of Breast Cancer Patients to Neoadjuvant Therapy

Neoadjuvant therapy (NAT) is an established treatment for certain high-risk, locally advanced, or unresectable breast cancers, often facilitating brea...

Understanding the Relationship Between Germ Layer Origin and Cancer Therapy Response: A Systematic Review

Cancer therapeutic response patterns may be fundamentally influenced by embryonic germ layer origin. Emerging evidence suggests mesoderm-derived malig...

Automatic variant prioritization in suspected genetic kidney disease using the Nephro Candidate Score (N-CS)

Despite the identification of >700 genes linked to rare and inherited kidney diseases (IKD), many individuals with presumed IKD do not receive a diagn...

EAGLE-AI: A large language model workflow for automated extraction and scoring of literature evidence linking genes to autism spectrum disorder

We previously developed the Evaluation of Autism Gene Link Evidence (EAGLE) manual curation framework and used it to characterise 219 autism-associate...

Advancing cardiovascular disease risk prediction beyond conventional methods: a systematic review of multimodal machine learning models integrating traditional clinical factors and multi-omics data

Cardiovascular disease (CVD) is a leading global health burden. Traditional risk prediction models, though widely used, often overlook genetic predisp...

A Glioma Stem Cell–Associated Transcriptomic Program Predicts Survival Across Adult and Pediatric High-Grade Gliomas

High-grade gliomas (HGGs), including adult glioblastoma (GBM) and pediatric diffuse intrinsic pontine gliomas (DIPGs), are sustained by glioma stem ce...

Fragile X Syndrome in Brazil: Development and Validation of a Clinical Checklist for Population Screening

Fragile X Syndrome (FXS) is the most common inherited cause of intellectual disability and syndromic autism, but diagnosis remains challenging due to ...

Robust methylome analysis and tumour–normal classification in TCGA–COAD: a reproducible workflow

Colorectal adenocarcinoma is caused in part by widespread epigenetic deregulation, yet the analysis of genome-wide DNA methylation of colorectal adeno...

Discovering latent subtypes of preterm birth and genetic risk using tensor decomposition on electronic health records

Preterm birth is a syndrome that is triggered by diverse biological pathways and presents with many comorbid diseases. Although twin studies reveal a ...

CanBART: A Generative Foundation Model of Cancer Molecular Alterations for Synthetic Patient Generation and Genomic Profile Completion

Despite the rapid expansion of genomic profiling in oncology, real-world datasets remain limited in size and unevenly distributed, particularly for ra...

Employing Consensus-Based Reasoning with Locally Deployed LLMs for Enabling Structured Data Extraction from Surgical Pathology Reports

Surgical pathology reports provide essential diagnostic information critical for cancer staging, treatment planning, and cancer registry documentation...

Modeling nonlinear and interaction effects of spatiotemporal and other non-genetic factors improves phenotypic prediction for complex traits

Adjusting for non-genetic factors can improve genetic association testing and polygenic prediction, yet most studies rely on linear adjustments for a ...