Autism

Delphi: Deep Learning for Polygenic Risk Prediction

Polygenic scores (PGS) are relative measures of an individual's genetic propensity to a particular trait or disease. Most PGS methods use a regression framework for polygenic modeling and assume that mutation effect estimates are constant across individuals. While these assumptions simplify computation, they increase error, and PGS are particularly less predictive for under-represented genetic anc...

Practical utility of sequence-to-omics models for improving the reproducibility of genetic fine-mapping

Recent advances in deep learning have led to the development of sequence-to-omics (S2O) models that predict molecular phenotypes directly from DNA sequences. Here, we systematically evaluate the utility of these models, e.g., AlphaGenome, Borzoi, Enformer, and Sei, for improving the reproducibility of genetic fine-mapping across expression quantitative trait loci (eQTL) datasets from Genotype-Tiss...

Mapping trans-eQTLs at single-cell resolution using Latent Interaction Variational Inference.

Single-cell expression quantitative trait loci (eQTL) studies hold promise for linking genetic variants to changes in gene expression in individual ce...

Early Pregnancy DNA Methylation Signatures as Predictors of Antenatal Depressive Symptoms: A longitudinal study of DNA methylation changes

Background. Antenatal depressive symptoms (ADS) are common and underdiagnosed, particularly in low and middle income countries, and are associated wit...

Prenatal Stress Detection from Electrocardiography Using Self-Supervised Deep Learning: Development and External Validation

Prenatal psychological stress affects 15-25% of pregnancies and increases risks of preterm birth, low birth weight, and adverse neurodevelopmental out...

G2DBridge: A Multimodal Framework Linking Genetics to Disease through Imaging Intermediates

Genetic-based risk prediction is becoming increasingly available for a wide range of common diseases thanks to the growth of large-scale biobanks and ...

Brain neuromarkers predict self- and other-related mentalizing across adult, clinical, and developmental samples

Human social interactions rely on the ability to reflect on one's own and others' internal states and traits--a process known as mentalizing. Impaired...

DVPNet: A New XAI-Based Interpretable Genetic Profiling Framework Using Nucleotide Transformer and Probabilistic Circuits

This research provides an XAI-driven genetic profiling approach that may contribute to scientific discoveries in genetic research. We propose a new ex...

SCOPE-PD: Explainable AI on Subjective and Clinical Objective Measurements of Parkinson's Disease for Precision Decision-Making

Parkinson's disease (PD) is a chronic and complex neurodegenerative disorder influenced by genetic, clinical, and lifestyle factors. Predicting this d...

Behavioral Assessment Reliability in Clinical Phenotyping and Biomarker Research for Autism

Autism Spectrum Disorder standardized behavioral assessments provide quantitative measures of symptoms, yet their reliability and consistency have not...

A Clinical Theory-Driven Deep Learning Model for Interpretable Autism Severity Prediction

Autism spectrum disorder (ASD) affects a substantial proportion of children worldwide, yet clinical assessment of symptom severity remains resource-in...

Computational Discovery of CRISPR-Cas13b Guide RNAs for Broad-Spectrum Dengue Virus Targeting

Dengue (DENV), an RNA virus, remains a significant global health threat, particularly in developing regions, with no widely effective antiviral therap...

Characterizing Highly Conserved Fragments in 3'UTRs via Computational and Transfer Learning Approaches

3' untranslated regions (3' UTRs) serve as regulatory platforms that modulate translation, mRNA localization, and stability through the binding of reg...

Pharmacogenomic Determinants of Post-Liver Transplant Diabetes Mellitus: A Systematic Review and In Silico Pharmacogenomic Analysis

Introduction: Tacrolimus remains central to liver transplantation, yet its narrow therapeutic index and pharmacokinetic variability are associated wit...

Deep learning-based neurodevelopmental assessment in preterm infants

Preterm infants (born between 28 and 37 weeks of gestation) face elevated risks of neurodevelopmental delays, making early identification crucial for ...

KOCOBrain: Kuramoto-Guided Graph Network for Uncovering Structure-Function Coupling in Adolescent Prenatal Drug Exposure

Exposure to psychoactive substances during pregnancy, such as cannabis, can disrupt neurodevelopment and alter large-scale brain networks, yet identif...

Metabolomic Biomarker Discovery for ADHD Diagnosis Using Interpretable Machine Learning

Attention Deficit Hyperactivity Disorder (ADHD) is a prevalent neurodevelopmental disorder with limited objective diagnostic tools, highlighting the u...

Leveraging Explainable Temporal-Modelling Machine Learning to Identify Distinct Multimorbidity Trajectory Profiles in Acute Myocardial Infarction

IntroductionAcute myocardial infarction (AMI) remains a leading cause of mortality, with the coexistence of other conditions (i.e., multimorbidity) co...

Integrative Multi-Omics Analysis Reveals Novel Molecular Signatures, Disease Stratification and Therapeutic Opportunities in Primary Ciliary Dyskinesia: First AI-ML empowered platform towards precision medicine targeting human ciliopathies

Primary ciliary dyskinesia (PCD) belongs to the group of rare genetic disorders that is extremely hard to diagnose and treat. Current diagnostic modal...

Multimodal attention fusion deep self-reconstruction presentation model for Alzheimer's disease diagnosis and biomarker identification.

The unknown pathogenic mechanisms of Alzheimer's disease (AD) make treatment challenging. Neuroimaging genetics offers a method for identifying diseas...