Identification of Autistic Risk Candidate Genes and Toxic Chemicals via Multilabel Learning

• Published in: IEEE transaction on neural networks and learning systems Vol. 32; no. 9; pp. 3971 - 3984
• Main Authors: Huang, Zhi-An, Zhang, Jia, Zhu, Zexuan, Wu, Edmond Q., Tan, Kay Chen
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Abnormalities; Algorithms; Autism; Autism Spectrum Disorder - chemically induced; Autism Spectrum Disorder - genetics; Autism spectrum disorders (ASDs); autistic biomarkers; Autistic Disorder - chemically induced; Autistic Disorder - genetics; Biochemical markers; Biological system modeling; Biomarkers; chemical toxicants; Chemicals; Classification; Computational modeling; Computational neuroscience; Computer Simulation; Data models; Databases, Genetic; Decisions; gene prioritization; Gene-Environment Interaction; Genes; Genetic abnormalities; Genetics; Hazardous Substances - classification; Hazardous Substances - toxicity; Humans; Machine Learning; multilabel classification (MLC); multilabel learning (MLL); Neural Networks, Computer; Neurodevelopmental disorders; Risk; Risk Assessment; Software; Toxicants

As a group of complex neurodevelopmental disorders, autism spectrum disorder (ASD) has been reported to have a high overall prevalence, showing an unprecedented spurt since 2000. Due to the unclear pathomechanism of ASD, it is challenging to diagnose individuals with ASD merely based on clinical obs...