@article{eabdd52438bd4cbe9fd3acdc7e06b3a9,
title = "Depression and suicide risk prediction models using blood-derived multi-omics data",
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.",
author = "Youngjune Bhak and Jeong, {Hyoung oh} and Cho, {Yun Sung} and Sungwon Jeon and Juok Cho and Gim, {Jeong An} and Yeonsu Jeon and Asta Blazyte and Park, {Seung Gu} and Kim, {Hak Min} and Shin, {Eun Seok} and Paik, {Jong Woo} and Lee, {Hae Woo} and Wooyoung Kang and Aram Kim and Yumi Kim and Kim, {Byung Chul} and Ham, {Byung Joo} and Jong Bhak and Semin Lee",
note = "Funding Information: We thank Prof. Yoon-Kyung Cho for supporting this project. We also thank Korea University Anam Hospital members for helping source blood and information of participants. Korea Institute of Science and Technology Information (KISTI) provided us with the Korea Research Environment Open NETwork (KREONET). This work was supported by the Civil-Military Dual-Use Technology Development Program (14-BR-SS-03) through the Agency for Defense Development; U-K BRAND Research Fund (1.190007.01) of UNIST; Research Project Funded by Ulsan City Research Fund (1.190033.01) of UNIST; the Next-Generation Information Computing Development Program through the National Research Foundation of Korea funded by the Ministry of Science and ICT (NRF-2016M3C4A7952635). Publisher Copyright: {\textcopyright} 2019, The Author(s).",
year = "2019",
month = dec,
day = "1",
doi = "10.1038/s41398-019-0595-2",
language = "English",
volume = "9",
journal = "Translational Psychiatry",
issn = "2158-3188",
publisher = "Nature Publishing Group",
number = "1",
}