Use of big data and machine learning algorithms to extract possible treatment targets in neurodevelopmental disorders

全基因组关联研究 机制(生物学) 大数据 数据科学 计算机科学 人工智能 认知科学 机器学习 神经科学 生物 心理学 遗传学 基因 单核苷酸多态性 认识论 操作系统 基因型 哲学
作者
Muhammad Ammar Malik,Stephen V. Faraone,Tom Michoel,Jan Haavik
出处
期刊:Pharmacology & Therapeutics [Elsevier]
卷期号:250: 108530-108530 被引量:2
标识
DOI:10.1016/j.pharmthera.2023.108530
摘要

Neurodevelopmental disorders (NDDs) impact multiple aspects of an individual's functioning, including social interactions, communication, and behaviors. The underlying biological mechanisms of NDDs are not yet fully understood, and pharmacological treatments have been limited in their effectiveness, in part due to the complex nature of these disorders and the heterogeneity of symptoms across individuals. Identifying genetic loci associated with NDDs can help in understanding biological mechanisms and potentially lead to the development of new treatments. However, the polygenic nature of these complex disorders has made identifying new treatment targets from genome-wide association studies (GWAS) challenging. Recent advances in the fields of big data and high-throughput tools have provided radically new insights into the underlying biological mechanism of NDDs. This paper reviews various big data approaches, including classical and more recent techniques like deep learning, which can identify potential treatment targets from GWAS and other omics data, with a particular emphasis on NDDs. We also emphasize the increasing importance of explainable and causal machine learning (ML) methods that can aid in identifying genes, molecular pathways, and more complex biological processes that may be future targets of intervention in these disorders. We conclude that these new developments in genetics and ML hold promise for advancing our understanding of NDDs and identifying novel treatment targets.
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