TC-VGC: A Tumor Classification System using Variations in Genes' Correlation

Authors:
Eunji Shin;Youngmi Yoon;Jaegyoon Ahn;Sanghyun Park
Affiliations:
Department of Computer Science, Yonsei University, 134 Sinchon-dong, Seodaemun-gu, Seoul 120-749, South Korea;Division of Information Technology, Gachon University of Medicine and Science, 534-2 Yonsu-dong, Yonsu-gu, Inchon 534-2, South Korea;Department of Computer Science, Yonsei University, 134 Sinchon-dong, Seodaemun-gu, Seoul 120-749, South Korea;Department of Computer Science, Yonsei University, 134 Sinchon-dong, Seodaemun-gu, Seoul 120-749, South Korea
Venue:
Computer Methods and Programs in Biomedicine
Year:
2011

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Abstract

Classification analysis of microarray data is widely used to reveal biological features and to diagnose various diseases, including cancers. Most existing approaches improve the performance of learning models by removing most irrelevant and redundant genes from the data. They select the marker genes which are expressed differently in normal and tumor tissues. These techniques ignore the importance of the complex functional-dependencies between genes. In this paper, we propose a new method for cancer classification which uses distinguished variations of gene-gene correlation in two sample groups. The cancer specific genetic network composed of these gene pairs contains many literature-curated prostate cancer genes, and we were successful in identifying new candidate prostate cancer genes inferred by them. Furthermore, this method achieved a high accuracy with a small number of genes in cancer classification.