Use of Big Data and Machine Learning Algorithms to Extract Possible Treatment Targets in Neurodevelopmental Disorders

39 Pages Posted: 22 May 2023

See all articles by Muhammad Ammar Malik

Muhammad Ammar Malik

University of Bergen

Stephen V. Faraone

State University of New York (SUNY) - Department of Psychiatry

Tom Michoel

University of Bergen

Jan Haavik

University of Bergen

Abstract

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 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 machine learning hold promise for advancing our understanding of NDDs and identifying novel treatment targets.

Note:
Funding Declaration: This study has received funding from the Research Council of Norway (Project No. 331725).

Conflicts of Interest: None

Keywords: genomics, neuropsychiatric disorders, machine learning, pharmacology, ADHD, autism

Suggested Citation

Malik, Muhammad Ammar and Faraone, Stephen V. and Michoel, Tom and Haavik, Jan, Use of Big Data and Machine Learning Algorithms to Extract Possible Treatment Targets in Neurodevelopmental Disorders. Available at SSRN: https://ssrn.com/abstract=4445320 or http://dx.doi.org/10.2139/ssrn.4445320

Muhammad Ammar Malik

University of Bergen ( email )

Muséplassen 1
N-5008 Bergen, +47 55 58
Norway

Stephen V. Faraone

State University of New York (SUNY) - Department of Psychiatry ( email )

Syracuse, NY
United States

Tom Michoel

University of Bergen ( email )

Jan Haavik (Contact Author)

University of Bergen ( email )

Muséplassen 1
N-5008 Bergen, +47 55 58
Norway

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