Article: An ensemble method for reconstructing gene regulatory network with jackknife resampling and arithmetic mean fusion Journal: International Journal of Data Mining and Bioinformatics (IJDMB) 2015 Vol.12 No.3 pp.328 - 342 Abstract: During the past decades, numerous computational approaches have been introduced for inferring the GRNs. PCA-CMI approach achieves the highest precision on the benchmark GRN datasets; however, it does not recover the meaningful edges that may have been deleted in an earlier iterative process. To recover this disadvantage and enhance the precision and robustness of GRNs inferred, we present an ensemble method, named as JRAMF, to infer GRNs from gene expression data by adopting two strategies of resampling and arithmetic mean fusion in this work. The jackknife resampling procedure were first employed to form a series of sub-datasets of gene expression data, then the PCA-CMI was used to generate the corresponding sub-networks from the sub-datasets, and the final GRN was inferred by integrating these sub-networks with an arithmetic mean fusion strategy. Compared with PCA-CMI algorithm, the results show that JRAMF outperforms significantly PCA-CMI method, which has a high and robust performance. Inderscience Publishers - linking academia, business and industry through research

Title: An ensemble method for reconstructing gene regulatory network with jackknife resampling and arithmetic mean fusion

Authors: Chen Zhou; Shao-Wu Zhang; Fei Liu

Addresses: College of Automation, Northwestern Polytechnical University, Xi'an 710072, China; Key Laboratory of Information Fusion Technology, Ministry of Education, Xi'an 710072, China ' College of Automation, Northwestern Polytechnical University, Xi'an 710072, China; Key Laboratory of Information Fusion Technology, Ministry of Education, Xi'an 710072, China ' College of Automation, Northwestern Polytechnical University, Xi'an 710072, China; Key Laboratory of Information Fusion Technology, Ministry of Education, Xi'an 710072, China

Abstract: During the past decades, numerous computational approaches have been introduced for inferring the GRNs. PCA-CMI approach achieves the highest precision on the benchmark GRN datasets; however, it does not recover the meaningful edges that may have been deleted in an earlier iterative process. To recover this disadvantage and enhance the precision and robustness of GRNs inferred, we present an ensemble method, named as JRAMF, to infer GRNs from gene expression data by adopting two strategies of resampling and arithmetic mean fusion in this work. The jackknife resampling procedure were first employed to form a series of sub-datasets of gene expression data, then the PCA-CMI was used to generate the corresponding sub-networks from the sub-datasets, and the final GRN was inferred by integrating these sub-networks with an arithmetic mean fusion strategy. Compared with PCA-CMI algorithm, the results show that JRAMF outperforms significantly PCA-CMI method, which has a high and robust performance.

Keywords: jackknife resampling; arithmetic mean fusion; ensemble method; gene regulatory networks; GRNs; network reconstruction; gene expression data; bioinformatics.

DOI: 10.1504/IJDMB.2015.069658

International Journal of Data Mining and Bioinformatics, 2015 Vol.12 No.3, pp.328 - 342

Received: 30 Jan 2014
Accepted: 08 Apr 2014
Published online: 29 May 2015 *

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Title: An ensemble method for reconstructing gene regulatory network with jackknife resampling and arithmetic mean fusion

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