Identification of the gene signature reflecting schizophrenia's etiology by constructing artificial intelligence-based method of enhanced reproducibility.

Journal: CNS neuroscience & therapeutics
PMID: 31350824

Abstract

AIMS: As one of the most fundamental questions in modern science, "what causes schizophrenia (SZ)" remains a profound mystery due to the absence of objective gene markers. The reproducibility of the gene signatures identified by independent studies is found to be extremely low due to the incapability of available feature selection methods and the lack of measurement on validating signatures' robustness. These irreproducible results have significantly limited our understanding of the etiology of SZ.

Authors

  • Qing-Xia Yang
    College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
  • Yun-Xia Wang
    College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
  • Feng-Cheng Li
    College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
  • Song Zhang
    College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
  • Yong-Chao Luo
    College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.
  • Yi Li
    Wuhan Zoncare Bio-Medical Electronics Co., Ltd, Wuhan, China.
  • Jing Tang
    Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
  • Bo Li
    Electric Power Research Institute, Yunnan Power Grid Co., Ltd., Kunming, Yunnan, China.
  • Yu-Zong Chen
    Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore, Singapore.
  • Wei-Wei Xue
    Innovative Drug Research and Bioinformatics Group, School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China.
  • Feng Zhu
    Department of Critical Care Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, People's Republic of China.

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