Journal Article2021Cited by 6

Use of relevancy and complementary information for discriminatory gene selection from high-dimensional gene expression data

Md Nazmul Haque, Sadia Sharmin, Amin Ahsan Ali, Abu Ashfaqur Sajib, Mohammad Shoyaib

Plos one

Public Library of Science San Francisco, CA USA, Vol. 16, Issue 10, pp. e0230164, ISBN: 1932-6203

  • Good Health and Well-being — Relevance: 86%
DOI: 10.1371/journal.pone.0230164Open AccessURL

Abstract

With the advent of high-throughput technologies, life sciences are generating a huge amount of varied biomolecular data. Global gene expression profiles provide a snapshot of all the genes that are transcribed in a cell or in a tissue under a particular condition. The high-dimensionality of such gene expression data (i.e., very large number of features/genes analyzed with relatively much less number of samples) makes it difficult to identify the key genes (biomarkers) that are truly attributing to a particular phenotype or condition, (such as cancer), de novo. For identifying the key genes from gene expression data, among the existing literature, mutual information (MI) is one of the most successful criteria. However, the correction of MI for finite sample is not taken into account in this regard. It is also important to incorporate dynamic discretization of genes for more relevant gene selection, although this is not considered in the available methods. Besides, it is usually suggested in current studies to remove redundant genes which is particularly inappropriate for biological data, as a group of genes may connect to each other for downstreaming proteins. Thus, despite being redundant, it is needed to add the genes which provide additional useful information for the disease. Addressing these issues, we proposed Mutual information based Gene Selection method (MGS) for selecting informative genes. Moreover, to rank these selected genes, we extended MGS and propose two ranking methods on the selected genes, such as MGSf—based on frequency and MGSrf—based on Random Forest. The proposed method not only obtained better classification rates on gene expression datasets derived from different gene expression studies compared to recently reported methods but also detected the key genes relevant to pathways with a causal relationship to the disease, which indicate that it will also able to find the responsible genes for an unknown disease data.

Keywords

  • Gene selection
  • Mutual information
  • finite-sample correction
  • gene expression data
  • high-dimensional data
  • Random forest
  • gene ranking
  • biomarkers
  • Cancer
  • gene redundancy

CCDS Authors

Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Amin Ahsan Ali, PhD

  • Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Research Interests

Artificial Intelligence, Machine Learning

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