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In silico markers: an evolutionary and statistical approach to select informative genes of human breast cancer subtypes.

Authors
  • Bhowmick, Shib Sankar1
  • Bhattacharjee, Debotosh2
  • Rato, Luis3
  • 1 Department of Electronics and Communication Engineering, Heritage Institute of Technology, Kolkata, 700107, India. [email protected] , (India)
  • 2 Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India. , (India)
  • 3 Department of Informatics, University of Evora, 7004-516, Evora, Portugal. , (Portugal)
Type
Published Article
Journal
Genes & genomics
Publication Date
Dec 01, 2019
Volume
41
Issue
12
Pages
1371–1382
Identifiers
DOI: 10.1007/s13258-019-00816-8
PMID: 31004329
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Recent advancement in bioinformatics offers the ability to identify informative genes from high dimensional gene expression data. Selection of informative genes from these large datasets has emerged as an issue of major concern among researchers. Gene functionality and regulatory mechanisms can be understood through the analysis of these gene expression data. Here, we present a computational method to identify informative genes for breast cancer subtypes such as Basal, human epidermal growth factor receptor 2 (Her2), luminal A (LumA), and luminal B (LumB). The proposed In Silico Markers method is a wrapper feature selection method based on Least Absolute Shrinkage and Selection Operator (LASSO), Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and Support Vector Machine (SVM) as a classifier. Moreover, the composite measure consisting of relevance, redundancy, and rank score of frequently appeared genes are used to select informative genes. The informative genes are validated by statistical and biologically relevant criteria. For a comparative evaluation of the proposed approach, biological similarity score designed on semantic similarity measure of GO terms are investigated. Further, the proposed technique is evaluated with 7 existing gene selection techniques using two-class annotated breast cancer subtype datasets. The utilization of this method can bring about the discovery of informative genes. Furthermore, under multiple criteria decision-making set-up, informative genes selected by the In Silico Markers are found to be admirable than the compared methods selected genes.

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