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

Eunji Shin, Youngmi Yoon, Jaegyoon Ahn, Sanghyun Park

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)e87-e101
JournalComputer Methods and Programs in Biomedicine
Issue number3
Publication statusPublished - 2011 Dec

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government(MEST) (No. 2011-0005154).

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Health Informatics


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