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Microarray classification and rule based cancer identification
conference contribution
posted on 2017-12-06, 00:00 authored by Jesmin Nahar, YP Chen, A B M Shawkat AliMicroarray analysis creates a clear scenario for the complete transcription profile of cells that facilitate drug and therapeutics development, disease diagnosis and enable us to take an in depth look at cell biology. One of the key challenges in microarray analysis, especially in cancerous gene expression profiles, is to identify genes or groups of genes that are highly responsible for the existence of a tumor in a cell. Our proposed modified algorithm Support Vector Machine (SVM) is used to classify cancer related 5 microarray data and observed improved performance than previously used Interesting Rule Group (IRG), Classification Based on Associations (CBA), and even a different version of SVM algorithm. Finally we use entropy measure through rule based learning algorithm to extract the responsible genes causes for cancer for each microarray problem. The rules are generated with higher acceptability.
Funding
Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)
History
Start Page
43End Page
46Number of Pages
4Start Date
2007-01-01ISBN-10
9843233948Location
Dhaka, BangladeshPublisher
ICICT 2007 Conference SecretariatPlace of Publication
Dhaka, BangladeshPeer Reviewed
- Yes
Open Access
- No
External Author Affiliations
Deakin University; Faculty of Business and Informatics;Era Eligible
- Yes