Genetics

Clinical Evaluation of an AI System for Streamlined Variant Interpretation in Genetic Testing

The growing use of exome/genome sequencing to diagnose hereditary diseases has increased the interpretive workload for clinical laboratories. Efficient methods are needed to maximize diagnostic yield without overwhelming resources. We developed DiagAI, an AI-powered system trained on 2.5 million ClinVar variants to predict ACMG pathogenicity classes. DiagAI ranks variants, proposes diagnostic shor...

AI enabled exome and transcriptome liquid biopsy platform spanning the continuum of care in oncology

Effective clinical management of patients with cancer requires highly accurate diagnosis, precise therapy selection, and highly sensitive monitoring of disease burden. Caris Assure is a multifunctional blood-based assay that couples whole exome and whole transcriptome sequencing on plasma and leukocytes with advanced machine learning techniques to satisfy all three clinical testing needs on one pl...

Artificial intelligence in clinical genetics: current practice and attitudes among the clinical genetics workforce

Artificial intelligence (AI) applications for clinical genetics hold the potential to improve patient care through supporting diagnostics and manageme...

Deep learning-based polygenic scores enhance generalizability of psychiatric disorders prediction

Polygenic scores (PGSs) have emerged as promising tools for predicting complex traits from genetic data, however, their predictive performance for psy...

Identification of (ultra-)rare functional promoter mutations in cancer using sequence-based deep learning models

The identification of non-coding somatic cancer-driver mutations remains challenging due to difficulties in interpreting rare and ultra-rare variants....

Transcriptomics-Driven Machine Learning Models Accurately Predict Chemotherapy Response in Muscle-invasive Bladder Cancer

Muscle-invasive bladder cancer (MIBC) is associated with poor predictability of response to cisplatin-based neoadjuvant chemotherapy (NAC). Consequent...

Multiple instance fine-mapping: predicting causal regulatory variants with a deep sequence model

Identifying causal genetic variants in a computational manner remains an open problem. Training end-to-end prediction models is not possible without l...

Integrative Machine Learning Approach to Risk Prediction for Dementia and Alzheimer’s Disease

Dementia, especially Alzheimer’s disease (AD), is a major global health challenge marked by progressive cognitive impairment, behavioral changes, and ...

Exploring Novel Kinetics of Automated H2O2 Nebulization: A Breakthrough in SARS-CoV-2 Elimination

Although hydrogen peroxide (H2O2) nebulization has shown promise for reducing SARS-CoV-2 loads in healthcare settings, its precise kinetics and real-w...

DeepSeek as the paradigm shift in rare disease diagnosis – the power of a fully automated genetic variant classification system

Large language models (LLMs) have been extensively tested for incorporating into medical applications in recent years, yet their potential in clinical...

Machine Learning-Based Identification of Sickle Cell Disease Subphenotypes in Clinical Trial Data

Sickle Cell Disease (SCD) is a rare autosomal recessive disorder caused by a point mutation producing abnormal hemoglobin S, leading to deformed red b...

Decoding the JAK-STAT axis in colorectal cancer with AI-HOPE-JAK-STAT: A conversational artificial intelligence approach to clinical-genomic integration

The Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathway is a critical mediator of immune regulation, inflammati...

Nucleotide motif-guided selection of plasma microRNA biomarkers for organ injury prediction in trauma

Trauma remains a leading cause of morbidity and mortality in part due to secondary organ injury and infection. Yet, our ability to predict the downstr...

Integrating GWAS and Transcriptomic Data Using PrediXcan and Multimodal Deep Learning Reveals Genetic Basis and Drug Repositioning Opportunities for Alzheimer’s Disease

Alzheimer’s disease (AD), the leading cause of dementia, imposes a significant societal and economic burden; however, its complex molecular mechanisms...

Evaluation of a Deep Learning and XAI based Facial Phenotyping Tool for Genetic Syndromes: A Clinical User Study

Artificial intelligence (AI) tools are increasingly employed in clinical genetics to assist in diagnosing genetic conditions by assessing photographs ...

Granular Insights:A Wastewater-Based Machine Learning Approach for Localized COVID-19 Hospitalization Forecasting

Wastewater based epidemiology (WBE) is a valuable tool for monitoring emerging disease trends in a community. Specifically, early predictions of hospi...

Decomposing patient heterogeneity of single-cell cancer data by cross-attention neural networks

Gene expression variation in cancer cells is attributed to many inherited and environmental factors, including genetic variants and cellular landscape...

Artificial intelligence for precision oncology: AI-HOPE-MAPK uncovers clinically actionable MAPK alterations in colorectal cancer

The emergence of early-onset colorectal cancer (EOCRC), particularly among populations with disproportionate health burdens, has exposed critical gaps...

Prediction of impulse control disorders in Parkinson’s disease: a longitudinal machine learning study

Impulse control disorders (ICD) in Parkinson’s disease (PD) patients mainly occur as adverse effects of dopamine replacement therapy. Despite several ...

AI-Driven Personalization of Dual Antiplatelet Therapy Duration Post-PCI: A Novel Approach Balancing Ischemic and Bleeding Risks

Precision-guided dual antiplatelet therapy (DAPT) duration post-percutaneous coronary intervention (PCI) remains a clinical challenge. Current risk st...