K-means clustering with infinite feature selection for classification tasks in gene expression data

Muhammad Akmal Remli, Kauthar Mohd Daud, Hui Wen Nies, Mohd Saberi Mohamad, Safaai Deris, Sigeru Omatu, Shahreen Kasim, Ghazali Sulong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)

Abstract

In the bioinformatics and clinical research areas, microarray technology has been widely used to distinguish a cancer dataset between normal and tumour samples. However, the high dimensionality of gene expression data affects the classification accuracy of an experiment. Thus, feature selection is needed to select informative genes and remove non-informative genes. Some of the feature selection methods, yet, ignore the interaction between genes. Therefore, the similar genes are clustered together and dissimilar genes are clustered in other groups. Hence, to provide a higher classification accuracy, this research proposed k-means clustering and infinite feature selection for identifying informative genes in the selected subset. This research has been applied to colorectal cancer and small round blue cell tumors datasets. Eventually, this research successfully obtained higher classification accuracy than the previous work.

Original languageEnglish
Title of host publication11th International Conference on Practical Applications of Computational Biology and Bioinformatics, 2017
EditorsMiguel Rocha, Juan F. De Paz, Tiago Pinto, Florentino Fdez-Riverola, Mohd Saberi Mohamad
PublisherSpringer Verlag
Pages50-57
Number of pages8
ISBN (Print)9783319608150
DOIs
Publication statusPublished - 2017
Externally publishedYes
Event11th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2017 - Porto, Portugal
Duration: Jun 21 2017Jun 23 2017

Publication series

NameAdvances in Intelligent Systems and Computing
Volume616
ISSN (Print)2194-5357

Conference

Conference11th International Conference on Practical Applications of Computational Biology and Bioinformatics, PACBB 2017
Country/TerritoryPortugal
CityPorto
Period6/21/176/23/17

Keywords

  • Artificial intelligence
  • Cancer classification
  • Gene expression data
  • Infinite feature selection
  • Informative genes
  • K-means clustering
  • Small round blue cell tumors

ASJC Scopus subject areas

  • Control and Systems Engineering
  • General Computer Science

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