ArticleBMC bioinformatics2018
iMEGES: integrated mental-disorder GEnome score by deep neural network for prioritizing the susceptibility genes for mental disorders in personal genomes.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Integrating Artificial Intelligence with Global Genomic Resources: A Narrative Review of Implications for Precision Medicine.Journal of multidisciplinary healthcare · 2026Review
- AI-powered precision medicine: utilizing genetic risk factor optimization to revolutionize healthcare.NAR genomics and bioinformatics · 2025Review
- Review
- Artificial intelligence in adolescents mental health disorder diagnosis, prognosis, and treatment.Frontiers in public health · 2023Article
- Deep learning in mental health outcome research: a scoping review.Translational psychiatry · 2020Article
- Reaching the End-Game for GWAS: Machine Learning Approaches for the Prioritization of Complex Disease Loci.Frontiers in genetics · 2020Review
- The International Conference on Intelligent Biology and Medicine (ICIBM) 2018: bioinformatics towards translational applications.BMC bioinformatics · 2018Article
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3 authors.
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Abstract
backgroundA range of rare and common genetic variants have been discovered to be potentially associated with mental diseases, but many more have not been uncovered. Powerful integrative methods are needed to systematically prioritize both variants and genes that confer susceptibility to mental diseases in personal genomes of individual patients and to facilitate the development of personalized treatment or therapeutic approaches.
methodsLeveraging deep neural network on the TensorFlow framework, we developed a computational tool, integrated Mental-disorder GEnome Score (iMEGES), for analyzing whole genome/exome sequencing data on personal genomes. iMEGES takes as input genetic mutations and phenotypic information from a patient with mental disorders, and outputs the rank of whole genome susceptibility variants and the prioritized disease-specific genes for mental disorders by integrating contributions from coding and non-coding variants, structural variants (SVs), known brain expression quantitative trait loci (eQTLs), and epigenetic information from PsychENCODE.
resultsiMEGES was evaluated on multiple datasets of mental disorders, and it achieved improved performance than competing approaches when large training dataset is available.
conclusioniMEGES can be used in population studies to help the prioritization of novel genes or variants that might be associated with the susceptibility to mental disorders, and also on individual patients to help the identification of genes or variants related to mental diseases.
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