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Bhak, Jong
The Genomics Institute of UNIST (TGI)
Research Interests
  • Geromics, genomics, bioinformatics, protein Engineering, OMICS

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Depression and suicide risk prediction models using blood-derived multi-omics data

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Title
Depression and suicide risk prediction models using blood-derived multi-omics data
Author
Bhak, YoungjuneJeong, Hyoung-ohCho, Yun SungJeon, SungwonCho, JuokGim, Jeong-AnJeon, YeonsuBlazyte, AstaPark, Seung GuKim, Hak-MinShin, Eun-SeokPaik, Jong-WooLee, Hae-WooKang, WooyoungKim, AramKim, YumiKim, Byung ChulHam, Byung-JooBhak, JongLee, Semin
Issue Date
2019-10
Publisher
Nature Publishing Group
Citation
TRANSLATIONAL PSYCHIATRY, v.9, pp.262
Abstract
More than 300 million people worldwide experience depression; annually, ~800,000 people die by suicide. Unfortunately, conventional interview-based diagnosis is insufficient to accurately predict a psychiatric status. We developed machine learning models to predict depression and suicide risk using blood methylome and transcriptome data from 56 suicide attempters (SAs), 39 patients with major depressive disorder (MDD), and 87 healthy controls. Our random forest classifiers showed accuracies of 92.6% in distinguishing SAs from MDD patients, 87.3% in distinguishing MDD patients from controls, and 86.7% in distinguishing SAs from controls. We also developed regression models for predicting psychiatric scales with R2 values of 0.961 and 0.943 for Hamilton Rating Scale for Depression–17 and Scale for Suicide Ideation, respectively. Multi-omics data were used to construct psychiatric status prediction models for improved mental health treatment.
URI
https://scholarworks.unist.ac.kr/handle/201301/27838
URL
https://www.nature.com/articles/s41398-019-0595-2
DOI
10.1038/s41398-019-0595-2
ISSN
2158-3188
Appears in Collections:
SLS_Journal Papers
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