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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Article number9177287
Pages (from-to)3971-3984
Number of pages14
Journal / PublicationIEEE Transactions on Neural Networks and Learning Systems
Volume32
Issue number9
Online published25 Aug 2020
Publication statusPublished - Sep 2021

Abstract

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 observations. Without additional support of biochemical markers, the difficulty of diagnosis could impact therapeutic decisions and, therefore, lead to delayed treatments. Recently, accumulating evidence have shown that both genetic abnormalities and chemical toxicants play important roles in the onset of ASD. In this work, a new multilabel classification (MLC) model is proposed to identify the autistic risk genes and toxic chemicals on a large-scale data set. We first construct the feature matrices and partially labeled networks for autistic risk genes and toxic chemicals from multiple heterogeneous biological databases. Based on both global and local measure metrics, the simulation experiments demonstrate that the proposed model achieves superior classification performance in comparison with the other state-of-the-art MLC methods. Through manual validation with existing studies, 60% and 50% out of the top-20 predicted risk genes are confirmed to have associations with ASD and autistic disorder, respectively. To the best of our knowledge, this is the first computational tool to identify ASD-related risk genes and toxic chemicals, which could lead to better therapeutic decisions of ASD.

Research Area(s)

  • Autism spectrum disorders (ASDs), autism, autistic biomarkers, chemical toxicants, gene prioritization, gene-environment interaction, multilabel classification (MLC), multilabel learning (MLL)

Citation Format(s)

Identification of Autistic Risk Candidate Genes and Toxic Chemicals via Multilabel Learning. / Huang, Zhi-An; Zhang, Jia; Zhu, Zexuan; Wu, Edmond Q; Tan, Kay Chen.

In: IEEE Transactions on Neural Networks and Learning Systems, Vol. 32, No. 9, 9177287, 09.2021, p. 3971-3984.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review