Implementation of parallel k-means algorithm for two-phase method biclustering in Carcinoma tumor gene expression data

Gianinna Ardaneswari, Alhadi B., Titin Siswantining

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

11 Citations (Scopus)

Abstract

Tumor is an abnormal growth of cells that serves no purpose. Carcinoma is a tumor that grows from the top of the cell membrane. In the field of molecular biology, the development of microarray technology is used in data store of disease genetic expression. For each of microarray gene, an amount of information is stored for each trait or condition. In gene expression data clustering can be done with a bicluster algorithm, that's clustering method not only the objects to be clustered, but also the properties or condition of the object. This research proposed a two-phase method for finding a bicluster. In the first phase, a parallel k-means algorithm is applied to the gene expression data. Then, in the second phase, Cheng and Church biclustering algorithm as one of biclustering method is performed to find biclusters. In this study, we discuss the implementation of two-phase method using biclustering of Cheng and Church and parallel k-means algorithm in Carcinoma tumor gene expression data. From the experimental results, we found five biclusters are formed by Carcinoma gene expression data.

Original languageEnglish
Title of host publicationSymposium on Biomathematics, SYMOMATH 2016
EditorsBeben Benyamin, Kasbawati
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735414938
DOIs
Publication statusPublished - 27 Mar 2017
Event4th International Symposium on Biomathematics, SYMOMATH 2016 - Makassar, Indonesia
Duration: 7 Oct 20169 Oct 2016

Publication series

NameAIP Conference Proceedings
Volume1825
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference4th International Symposium on Biomathematics, SYMOMATH 2016
CountryIndonesia
CityMakassar
Period7/10/169/10/16

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