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

  • Shib Sankar BhowmickEmail author
  • Debotosh Bhattacharjee
  • Luis Rato
Research Article
  • 30 Downloads

Abstract

Background

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.

Objective

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).

Methods

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.

Results

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.

Conclusion

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.

Keywords

Breast cancer subtype Biological analysis Gene selection Messenger RNA Statistical analysis 

Notes

Compliance with ethical standards

Conflict of interest

Shib Sankar Bhowmick, Debotosh Bhattacharjee and Luis Rato declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

13258_2019_816_MOESM1_ESM.pdf (856 kb)
Supplementary material 1 (PDF 856 kb)
13258_2019_816_MOESM2_ESM.pdf (55 kb)
Supplementary material 2 (PDF 56 kb)
13258_2019_816_MOESM3_ESM.pdf (54 kb)
Supplementary material 3 (PDF 54 kb)
13258_2019_816_MOESM4_ESM.pdf (40 kb)
Supplementary material 3 (PDF 41 kb)

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Copyright information

© The Genetics Society of Korea 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringHeritage Institute of TechnologyKolkataIndia
  2. 2.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  3. 3.Department of InformaticsUniversity of EvoraEvoraPortugal

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