Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder.

ABSTRACT: Although the efficacy of pharmacy in the treatment of attention deficit/hyperactivity disorder (ADHD) has been well established, the lack of predictors of treatment response poses great challenges for personalized treatment. The current study employed a comprehensive approach, combining genome-wide association analyses (GWAS) and deep learning (DL) methods, to elucidate the genetic underpinnings of pharmacological treatment response in ADHD. Based on genotype data of medication-naïve patients with ADHD who received pharmacological treatments for 12 weeks, the current study performed GWAS using the percentage changes in ADHD-RS score as phenotype. Then, DL models were constructed to predict percentage changes in symptom scores using genetic variants selected based on four different genome-w

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC11806042 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature