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Identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence‐based method of enhanced reproducibility
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 incapabilit...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6698965/ https://www.ncbi.nlm.nih.gov/pubmed/31350824 http://dx.doi.org/10.1111/cns.13196 |
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author | Yang, Qing‐Xia Wang, Yun‐Xia Li, Feng‐Cheng Zhang, Song Luo, Yong‐Chao Li, Yi Tang, Jing Li, Bo Chen, Yu‐Zong Xue, Wei‐Wei Zhu, Feng |
author_facet | Yang, Qing‐Xia Wang, Yun‐Xia Li, Feng‐Cheng Zhang, Song Luo, Yong‐Chao Li, Yi Tang, Jing Li, Bo Chen, Yu‐Zong Xue, Wei‐Wei Zhu, Feng |
author_sort | Yang, Qing‐Xia |
collection | PubMed |
description | 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. METHODS: In this study, a new feature selection strategy was developed, and a comprehensive analysis was then conducted to ensure a reliable signature discovery. Particularly, the new strategy (a) combined multiple randomized sampling with consensus scoring and (b) assessed gene ranking consistency among different datasets, and a comprehensive analysis among nine independent studies was conducted. RESULTS: Based on a first‐ever evaluation of methods’ reproducibility that was cross‐validated by nine independent studies, the newly developed strategy was found to be superior to the traditional ones. As a result, 33 genes were consistently identified from multiple datasets by the new strategy as differentially expressed, which might facilitate our understanding of the mechanism underlying the etiology of SZ. CONCLUSION: A new strategy capable of enhancing the reproducibility of feature selection in current SZ research was successfully constructed and validated. A group of candidate genes identified in this study should be considered as great potential for revealing the etiology of SZ. |
format | Online Article Text |
id | pubmed-6698965 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-66989652019-08-29 Identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence‐based method of enhanced reproducibility Yang, Qing‐Xia Wang, Yun‐Xia Li, Feng‐Cheng Zhang, Song Luo, Yong‐Chao Li, Yi Tang, Jing Li, Bo Chen, Yu‐Zong Xue, Wei‐Wei Zhu, Feng CNS Neurosci Ther Original Articles 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. METHODS: In this study, a new feature selection strategy was developed, and a comprehensive analysis was then conducted to ensure a reliable signature discovery. Particularly, the new strategy (a) combined multiple randomized sampling with consensus scoring and (b) assessed gene ranking consistency among different datasets, and a comprehensive analysis among nine independent studies was conducted. RESULTS: Based on a first‐ever evaluation of methods’ reproducibility that was cross‐validated by nine independent studies, the newly developed strategy was found to be superior to the traditional ones. As a result, 33 genes were consistently identified from multiple datasets by the new strategy as differentially expressed, which might facilitate our understanding of the mechanism underlying the etiology of SZ. CONCLUSION: A new strategy capable of enhancing the reproducibility of feature selection in current SZ research was successfully constructed and validated. A group of candidate genes identified in this study should be considered as great potential for revealing the etiology of SZ. John Wiley and Sons Inc. 2019-07-27 /pmc/articles/PMC6698965/ /pubmed/31350824 http://dx.doi.org/10.1111/cns.13196 Text en © 2019 The Authors. CNS Neuroscience & Therapeutics Published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Articles Yang, Qing‐Xia Wang, Yun‐Xia Li, Feng‐Cheng Zhang, Song Luo, Yong‐Chao Li, Yi Tang, Jing Li, Bo Chen, Yu‐Zong Xue, Wei‐Wei Zhu, Feng Identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence‐based method of enhanced reproducibility |
title | Identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence‐based method of enhanced reproducibility |
title_full | Identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence‐based method of enhanced reproducibility |
title_fullStr | Identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence‐based method of enhanced reproducibility |
title_full_unstemmed | Identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence‐based method of enhanced reproducibility |
title_short | Identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence‐based method of enhanced reproducibility |
title_sort | identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence‐based method of enhanced reproducibility |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6698965/ https://www.ncbi.nlm.nih.gov/pubmed/31350824 http://dx.doi.org/10.1111/cns.13196 |
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