Genetics

Evaluating Genetic-Based Disease Prediction Approaches Through Simulation

Common diseases exhibit substantial heritability, and GWAS of these diseases have revealed hundreds of thousands of high-frequency disease susceptibility variants throughout the genome. These studies offer the prospect of using genomic data to improve disease prediction and diagnosis, however, the relative performance of different predictive modeling approaches is not well-characterized. To invest...

Integration of Deep Learning Annotations with Functional Genomics Improves Identification of Causal Alzheimer’s Disease Variants

Genetic variants associated with Alzheimer’s disease (AD) through genome-wide association studies (GWAS) are challenging to interpret because most lie in non-coding regions of the genome. Here, a method was developed that integrates deep learning variant effect prediction (DL-VEP) scores from Enformer, DeepSea, and ChromBPNet models with cell-type specific regulatory annotations to improve fine-ma...

An Actor-Critic Reinforcement Learning Framework for Variant Evidence Interpretation

We present a reinforcement learning (RL) framework that uses established genomic metrics – such as the GuRu score, variant/gene risk priors, and popul...

Training with synthetic data provides accurate and openly-available DNA methylation classifiers for developmental disorders and congenital anomalies via MethaDory

Multiple developmental and congenital disorders due to genetic variants or environmental exposures are associated with unique genome-wide alterations ...

Large language models identify causal genes in complex trait GWAS

Identifying causal genes at genome-wide association study (GWAS) loci remains a major challenge. Literature evidence for disease-gene co-occurrence, w...

CNNeoPP: A Deep Learning Pipeline for Personalized Neoantigen Prediction and Liquid Biopsy Applications

Neoantigens have emerged as promising targets for personalized cancer immunotherapy. However, accurate identification of immunogenic neoantigens remai...

Neuroimaging-AI endophenotypes reveal underlying mechanisms and genetic factors contributing to progression and development of four brain disorders

Recent work leveraging artificial intelligence has offered promise to dissect disease heterogeneity by identifying complex intermediate brain phenotyp...

NTDscope: A multi-contrast portable microscope for disease diagnosis

Accurate diagnostics are essential for disease control and elimination efforts. However, access to diagnostics for neglected tropical diseases (NTDs) ...

Detection of patient metadata in published articles for genomic epidemiology using machine learning and large language models

Patient metadata exist in published articles, but are often dis-connected from genome sequences in databases, limiting their utility for genomic epide...

Biological sex affects gene expression and functional variation across the human genome

Humans display sexual dimorphism across many traits, but little is known about underlying genetic mechanisms and impacts on disease. We utilized singl...

Predicting Survivability of Cancer Patients with Metastatic Patterns Using Explainable AI

Cancer remains a leading global health challenge and a major cause of mortality. This study leverages machine learning (ML) to predict the survivabili...

Early Detection of Autism Spectrum Disorder in Children Using Different Machine Learning Algorithms

Autism spectrum disorder(ASD) is a neurological condition marked by impaired communication abilities, social detachment, and repetitive behaviors in i...

The gSOS Polygenic Score is Associated with Bone Density and Fracture Risk in Childhood

The polygenic risk score genetic quantitative ultrasound speed of sound (gSOS) was developed using machine learning algorithms in adults of European a...

Cell-free DNA methylome and fragmentome analysis for disease relapse monitoring in patients with Ewing Sarcoma

Liquid biopsies and cell-free DNA (cfDNA) offer minimally invasive methods for the diagnosis and monitoring of Ewing Sarcoma (EwS). EwS have a low tum...

A Novel Multi-Omics Deep Learning Framework for Spatiotemporal Cerebral Cortex Localization & Expression

Accurately mapping and predicting amino acid localization and gene expression patterns in the dorsolateral prefrontal cortex (DLPFC) is important for ...

Towards a diagnostic test for sporadic ALS utilising deep learning and SNP microarrays

A variety of common and rare genetic factors have been implicated in the development of amyotrophic lateral sclerosis (ALS), and the evidence is that ...

Deciphering epistatic genetic regulation of cardiac hypertrophy

Although genetic variant effects often interact non-additively, strategies to uncover epistasis remain in their infancy. Here, we develop low-signal s...

Federated Learning for the pathogenicity annotation of genetic variants in multi-site clinical settings

Rare diseases collectively affect 5% of the population. However, fewer than 50% of rare disease patients receive a molecular diagnosis after whole gen...

Cross-Disorder Machine Learning Uncovers Schizophrenia Risk Variants Predictive of Alzheimer’s Disease

Alzheimer’s disease (AD) and Schizophrenia (SCZ) exhibit overlapping clinical features and biological mechanisms, but the extent of their shared genet...

From Text to Translation: Using Language Models to Prioritize Variants for Clinical Review

Despite rapid advances in genomic sequencing, most rare genetic variants remain insufficiently characterized for clinical use, limiting the potential ...