Unravelling the GSK3β-related genotypic interaction network influencing hippocampal volume in recurrent major depressive disorder

Inkster, Becky; Simmons, Andy; Cole, James R.; Schoof, Erwin M.; Linding, Rune; Nichols, Thomas E.; Muglia, Pierandrea; Holsboer, Florian; Sämann, Philipp G.; McGuffin, Peter; Fu, Cynthia H.Y.; Miskowiak, Kamilla Woznica; Matthews, Paul M.; Zai, Gwyneth; Nicodemus, Kristin K.

doi:10.1097/ypg.0000000000000203

Cited by 28 publications

References 65 publications

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Machine Learning as a Tool to Find New Pharmacological Targets in Mood Disorders: A Systematic Review

Romão,

Melo,

André

et al. 2024

Curr Treat Options Psych

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Purpose of Review Mood disorders (MD) are mental disorders that need accurate diagnosis and proper treatment. Growing volume of data from neurobehavioral sciences is becoming complex for traditional research to analyze. New drugs’ slow development fails to meet the needs of neurobehavioral disorders. Machine Learning (ML) techniques support research by refining the detection, diagnosis, treatment, and research, and are being employed to expedite the discovery of pharmacological targets. This review aims to assess evidence regarding the contribution of ML in finding new pharmacological targets in adults with MD. Recent findings The most significant area of research amongst MD is major depressive disorder. ML identified target gene candidates, pathways and biomarkers related to MD, which can pave the way for promising therapeutic strategies. ML was also found to enhance diagnostic accuracy. Summary ML techniques have the potential to bridge the gap between biological data and chemical drug information, providing new discoveries in pharmacological agents.

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