PMID- 33986383
OWN - NLM
STAT- MEDLINE
DCOM- 20211101
LR  - 20211101
IS  - 2045-2322 (Electronic)
IS  - 2045-2322 (Linking)
VI  - 11
IP  - 1
DP  - 2021 May 13
TI  - Prediction of functional outcomes of schizophrenia with genetic biomarkers using 
      a bagging ensemble machine learning method with feature selection.
PG  - 10179
LID - 10.1038/s41598-021-89540-6 [doi]
LID - 10179
AB  - Genetic variants such as single nucleotide polymorphisms (SNPs) have been 
      suggested as potential molecular biomarkers to predict the functional outcome of 
      psychiatric disorders. To assess the schizophrenia' functional outcomes such as 
      Quality of Life Scale (QLS) and the Global Assessment of Functioning (GAF), we 
      leveraged a bagging ensemble machine learning method with a feature selection 
      algorithm resulting from the analysis of 11 SNPs (AKT1 rs1130233, COMT rs4680, 
      DISC1 rs821616, DRD3 rs6280, G72 rs1421292, G72 rs2391191, 5-HT2A rs6311, MET 
      rs2237717, MET rs41735, MET rs42336, and TPH2 rs4570625) of 302 schizophrenia 
      patients in the Taiwanese population. We compared our bagging ensemble machine 
      learning algorithm with other state-of-the-art models such as linear regression, 
      support vector machine, multilayer feedforward neural networks, and random 
      forests. The analysis reported that the bagging ensemble algorithm with feature 
      selection outperformed other predictive algorithms to forecast the QLS functional 
      outcome of schizophrenia by using the G72 rs2391191 and MET rs2237717 SNPs. 
      Furthermore, the bagging ensemble algorithm with feature selection surpassed 
      other predictive algorithms to forecast the GAF functional outcome of 
      schizophrenia by using the AKT1 rs1130233 SNP. The study suggests that the 
      bagging ensemble machine learning algorithm with feature selection might present 
      an applicable approach to provide software tools for forecasting the functional 
      outcomes of schizophrenia using molecular biomarkers.
FAU - Lin, Eugene
AU  - Lin E
AD  - Department of Biostatistics, University of Washington, Seattle, WA, 98195, USA.
AD  - Department of Electrical and Computer Engineering, University of Washington, 
      Seattle, WA, 98195, USA.
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. cyndi36@gmail.com.
AD  - Department of Psychiatry, Kaohsiung Chang Gung Memorial Hospital, Chang Gung 
      University College of Medicine, Kaohsiung, Taiwan. cyndi36@gmail.com.
AD  - School of Medicine, Chang Gung University, Taoyuan, Taiwan. cyndi36@gmail.com.
FAU - Lane, Hsien-Yuan
AU  - Lane HY
AD  - Graduate Institute of Biomedical Sciences, China Medical University, Taichung, 
      Taiwan. hylane@gmail.com.
AD  - Department of Psychiatry, China Medical University Hospital, Taichung, Taiwan. 
      hylane@gmail.com.
AD  - Brain Disease Research Center, China Medical University Hospital, Taichung, 
      Taiwan. hylane@gmail.com.
AD  - Department of Psychology, College of Medical and Health Sciences, Asia 
      University, Taichung, Taiwan. hylane@gmail.com.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20210513
PL  - England
TA  - Sci Rep
JT  - Scientific reports
JID - 101563288
RN  - 0 (Genetic Markers)
SB  - IM
MH  - Adolescent
MH  - Adult
MH  - Aged
MH  - Genetic Markers
MH  - Humans
MH  - *Machine Learning
MH  - Middle Aged
MH  - Polymorphism, Genetic
MH  - Psychosocial Functioning
MH  - Quality of Life
MH  - Schizophrenia/*genetics
MH  - Young Adult
PMC - PMC8119477
COIS- The authors declare no competing interests.
EDAT- 2021/05/15 06:00
MHDA- 2021/11/03 06:00
PMCR- 2021/05/13
CRDT- 2021/05/14 06:35
PHST- 2021/02/11 00:00 [received]
PHST- 2021/04/27 00:00 [accepted]
PHST- 2021/05/14 06:35 [entrez]
PHST- 2021/05/15 06:00 [pubmed]
PHST- 2021/11/03 06:00 [medline]
PHST- 2021/05/13 00:00 [pmc-release]
AID - 10.1038/s41598-021-89540-6 [pii]
AID - 89540 [pii]
AID - 10.1038/s41598-021-89540-6 [doi]
PST - epublish
SO  - Sci Rep. 2021 May 13;11(1):10179. doi: 10.1038/s41598-021-89540-6.