An improved parallelized mRMR for gene subset selection in cancer classification

Rohani Mohammad Kusairi, Kohbalan Moorthy, Habibollah Haron, Mohd Saberi Mohamad, Suhaimi Napis, Shahreen Kasim

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


DNA microarray technology has become a more attractive tool for cancer classification in the scientific and industrial fields. Based on the previous researchers, the conventional approach for cancer classification is primarily based on the morphological appearance of the tumor. The limitations of this approach are the bias in identify the tumors by expert and faced the difficulty in differentiating the cancer subtypes due to most cancers being highly related to the specific biological insight. Thus, this study proposes an improved parallelized Minimum Redundancy Maximum Relevance (mRMR), which is a particularly fast feature selection method for finding a set of both relevant and complementary features. The mRMR can identify genes more relevance to the biological context that leads to richer biological interpretations. The proposed method is expected to achieve accurate classification performance using a small number of predictive genes when tested using two datasets from Cancer Genome Project and compared to previous methods.

Original languageEnglish
Pages (from-to)1595-1600
Number of pages6
JournalInternational Journal on Advanced Science, Engineering and Information Technology
Issue number4-2 Special Issue
Publication statusPublished - 2017
Externally publishedYes


  • Cancer classification
  • Feature selection
  • MRMR filter method
  • Parallelized mRMR
  • Random forest classifier

ASJC Scopus subject areas

  • General Computer Science
  • General Agricultural and Biological Sciences
  • General Engineering


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