Identification of gene expression linked to malignancy of human colorectal carcinoma using restricted boltzmann machines

Arida F. Syafiandini, Mukhlis Amien, Ito Wasito, Setiadi Yazid, Aries Fitriawan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Learning hidden information or pattern on gene expression data to uncover an underlying molecular features is called gene expression profiling. To perform gene expression profiling, an unsupervised machine learning method can be employed. In this paper, Gaussian RBM is proposed to obtain the optimal number of clusters and their members on human colorectal cancer dataset provided by Muro. Gaussian RBM forms two large numbers of genes clusters and one smaller cluster which has several tumour-classifier genes as its members. The two large numbers of genes clusters formed by Gaussian RBM succeed in showing a significant correlation with the existence of tumour and distant metastasis but they show no significant correlation with lymph node metastasis existence. The smaller number of genes clusters gives a statistically significant result in clustering patients into two groups.

Original languageEnglish
Title of host publicationProceedings of the 7th International Conference on Bioscience, Biochemistry and Bioinformatics, ICBBB 2017
PublisherAssociation for Computing Machinery
Pages17-21
Number of pages5
ISBN (Electronic)9781450348324
DOIs
Publication statusPublished - 21 Jan 2017
Event7th International Conference on Bioscience, Biochemistry and Bioinformatics, ICBBB 2017 - Bangkok, Thailand
Duration: 21 Jan 201723 Jan 2017

Publication series

NameACM International Conference Proceeding Series

Conference

Conference7th International Conference on Bioscience, Biochemistry and Bioinformatics, ICBBB 2017
CountryThailand
CityBangkok
Period21/01/1723/01/17

Keywords

  • Gene expression
  • Human colorectal
  • Malignancy
  • RBM

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