PMID- 32582679
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20200928
IS  - 2296-4185 (Print)
IS  - 2296-4185 (Electronic)
IS  - 2296-4185 (Linking)
VI  - 8
DP  - 2020
TI  - An Ensemble Approach to Predict Schizophrenia Using Protein Data in the 
      N-methyl-D-Aspartate Receptor (NMDAR) and Tryptophan Catabolic Pathways.
PG  - 569
LID - 10.3389/fbioe.2020.00569 [doi]
LID - 569
AB  - In the wake of recent advances in artificial intelligence research, precision 
      psychiatry using machine learning techniques represents a new paradigm. The 
      D-amino acid oxidase (DAO) protein and its interaction partner, the D-amino acid 
      oxidase activator (DAOA, also known as G72) protein, have been implicated as two 
      key proteins in the N-methyl-D-aspartate receptor (NMDAR) pathway for 
      schizophrenia. Another potential biomarker in regard to the etiology of 
      schizophrenia is melatonin in the tryptophan catabolic pathway. To develop an 
      ensemble boosting framework with random undersampling for determining disease 
      status of schizophrenia, we established a prediction approach resulting from the 
      analysis of genomic and demographic variables such as DAO levels, G72 levels, 
      melatonin levels, age, and gender of 355 schizophrenia patients and 86 unrelated 
      healthy individuals in the Taiwanese population. We compared our ensemble 
      boosting framework with other state-of-the-art algorithms such as support vector 
      machine, multilayer feedforward neural networks, logistic regression, random 
      forests, naive Bayes, and C4.5 decision tree. The analysis revealed that the 
      ensemble boosting model with random undersampling [area under the receiver 
      operating characteristic curve (AUC) = 0.9242 +/- 0.0652; sensitivity = 0.8580 +/- 
      0.0770; specificity = 0.8594 +/- 0.0760] performed maximally among predictive 
      models to infer the complicated relationship between schizophrenia disease status 
      and biomarkers. In addition, we identified a causal link between DAO and G72 
      protein levels in influencing schizophrenia disease status. The study indicates 
      that the ensemble boosting framework with random undersampling may provide a 
      suitable method to establish a tool for distinguishing schizophrenia patients 
      from healthy controls using molecules in the NMDAR and tryptophan catabolic 
      pathways.
CI  - Copyright (c) 2020 Lin, Lin, Hung and Lane.
FAU - Lin, Eugene
AU  - Lin E
AD  - Department of Biostatistics, University of Washington, Seattle, WA, United 
      States.
AD  - Department of Electrical & Computer Engineering, University of Washington, 
      Seattle, WA, United States.
AD  - Graduate Institute of Biomedical Sciences, China Medical University, Taichung, 
      Taiwan.
FAU - Lin, Chieh-Hsin
AU  - Lin CH
AD  - Graduate Institute of Biomedical Sciences, China Medical University, Taichung, 
      Taiwan.
AD  - Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung 
      University College of Medicine, Kaohsiung, Taiwan.
AD  - School of Medicine, Chang Gung University, Taoyuan, Taiwan.
FAU - Hung, Chung-Chieh
AU  - Hung CC
AD  - Department of Psychiatry, China Medical University Hospital, Taichung, Taiwan.
FAU - Lane, Hsien-Yuan
AU  - Lane HY
AD  - Graduate Institute of Biomedical Sciences, China Medical University, Taichung, 
      Taiwan.
AD  - Department of Psychiatry, China Medical University Hospital, Taichung, Taiwan.
AD  - Brain Disease Research Center, China Medical University Hospital, Taichung, 
      Taiwan.
AD  - Department of Psychology, College of Medical and Health Sciences, Asia 
      University, Taichung, Taiwan.
LA  - eng
PT  - Journal Article
DEP - 20200604
PL  - Switzerland
TA  - Front Bioeng Biotechnol
JT  - Frontiers in bioengineering and biotechnology
JID - 101632513
PMC - PMC7287032
OTO - NOTNLM
OT  - N-methyl-D-aspartate receptor
OT  - ensemble boosting
OT  - multilayer feedforward neural networks
OT  - precision psychiatry
OT  - schizophrenia
EDAT- 2020/06/26 06:00
MHDA- 2020/06/26 06:01
PMCR- 2020/01/01
CRDT- 2020/06/26 06:00
PHST- 2019/10/28 00:00 [received]
PHST- 2020/05/11 00:00 [accepted]
PHST- 2020/06/26 06:00 [entrez]
PHST- 2020/06/26 06:00 [pubmed]
PHST- 2020/06/26 06:01 [medline]
PHST- 2020/01/01 00:00 [pmc-release]
AID - 10.3389/fbioe.2020.00569 [doi]
PST - epublish
SO  - Front Bioeng Biotechnol. 2020 Jun 4;8:569. doi: 10.3389/fbioe.2020.00569. 
      eCollection 2020.