Implementation of the Binary Inclusion-Maximal Biclustering Algorithm on Adenoma Microarray Gene Expression Data

Syamira Merina, Alhadi B., Gianinna Ardaneswari

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

Abstract

Adenoma is a benign type of tumor in the epidermal layer of tissue. Adenoma can turn into malignant cancer which is then called Adenocarcinoma. There is a form of molecular biology data which is developing today, namely microarray gene expression data. Microarray can be used for detection and research in the field of oncology. One method for processing and analyzing microarray gene data is by biclustering. In this study, the writer will be using one method of biclustering, the Binary Inclusion-Maximal algorithm, and implement it on microarray gene expression data. The algorithm will be performed on Colon Adenoma data consisting of 7070 genes with four adenoma cell samples and four normal cell samples. The implementation took less than one second and resulted in 22 biclusters composed of 25 genes.

Original languageEnglish
Title of host publication2018 2nd International Conference on Informatics and Computational Sciences, ICICoS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages159-164
Number of pages6
ISBN (Electronic)9781538674406
DOIs
Publication statusPublished - 22 Jan 2019
Event2nd International Conference on Informatics and Computational Sciences, ICICoS 2018 - Semarang, Indonesia
Duration: 30 Oct 201831 Oct 2018

Publication series

Name2018 2nd International Conference on Informatics and Computational Sciences, ICICoS 2018

Conference

Conference2nd International Conference on Informatics and Computational Sciences, ICICoS 2018
Country/TerritoryIndonesia
CitySemarang
Period30/10/1831/10/18

Keywords

  • adenoma
  • biclustering
  • binary inclusion-maximal
  • microarray

Fingerprint

Dive into the research topics of 'Implementation of the Binary Inclusion-Maximal Biclustering Algorithm on Adenoma Microarray Gene Expression Data'. Together they form a unique fingerprint.

Cite this