Universiti Teknologi Malaysia Institutional Repository

Multiple gene sets for cancer classification using gene range selection based on random forest

Moorthy, K. and Mohamad, M. S. and Deris, S. (2013) Multiple gene sets for cancer classification using gene range selection based on random forest. In: Lecture Notes In Computer Science (Including Subseries Lecture Notes In Artificial Intelligence And Lecture Notes In Bioinformatics).

Full text not available from this repository.

Official URL: http://dx.doi.org/10.1007/978-3-642-36546-1_40


The advancement of microarray technology allows obtaining genetic information from cancer patients, as computational data and cancer classification through computation software, has become possible. Through gene selection, we can identify certain numbers of informative genes that can be grouped into a smaller sets or subset of genes; which are informative genes taken from the initial data for the purpose of classification. In most available methods, the amount of genes selected in gene subsets are dependent on the gene selection technique used and cannot be fine-tuned to suit the requirement for particular number of genes. Hence, a proposed technique known as gene range selection based on a random forest method allows selective subset for better classification of cancer datasets. Our results indicate that various gene sets assist in increasing the overall classification accuracy of the cancer related datasets, as the amount of genes can be further scrutinized to create the best subset of genes. Moreover, it can assist the gene-filtering technique for further analysis of the microarray data in gene network analysis, gene-gene interaction analysis and many other related fields.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Gene Selection, Cancer Classification, Random Forest, Gene Expression, Microarray Data
Subjects:Q Science > QR Microbiology
ID Code:51187
Deposited By: Haliza Zainal
Deposited On:27 Jan 2016 09:53
Last Modified:15 Aug 2017 16:02

Repository Staff Only: item control page