Yanli Zhang‐James

Integrating Genetic, Environmental, Cognitive, and Temperament Data for ADHD Prediction in Explainable Deep Learning Models

This study develops an explainable deep learning model to predict ADHD using genetic, environmental, cognitive, and temperament data, finding that temperament features significantly improve classification performance.

Using Genomic Context Informed Genotype Data and Within-model Ancestry Adjustment to Classify Type 2 Diabetes

This paper develops a framework using genomic context and genotype data in convolutional neural networks to predict type 2 diabetes risk, showing improved performance over polygenic risk scores.

Improving Machine Learning Prediction of ADHD Using Gene Set Polygenic Risk Scores and Risk Scores from Genetically Correlated Phenotypes

A Systematic Review of the Etiology and Neurobiology of Intermittent Explosive Disorder

This review identifies a multifactorial etiology and neurobiology of IED, highlighting the amygdala and orbitofrontal cortex, serotonergic signaling, and childhood trauma as key factors.

Evidence for Similar Structural Brain Anomalies in Youth and Adult Attention-Deficit/Hyperactivity Disorder: A Machine Learning Analysis

This study uses machine learning to show that structural brain anomalies in ADHD are similar in children and adults, supporting continuity of the disorder's pathophysiology.

Machine-Learning Prediction of Comorbid Substance Use Disorders in ADHD Youth Using Swedish Registry Data

This study uses Swedish registry data to predict substance use disorders in ADHD youth with machine learning models, achieving moderate accuracy even without prior SUD diagnosis.