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

Eunji Shin, Youngmi Yoon, Jaegyoon Ahn, Sanghyun Park

Research output: Contribution to journalArticle

6 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)e87-e101
JournalComputer Methods and Programs in Biomedicine
Volume104
Issue number3
DOIs
Publication statusPublished - 2011 Dec 1

Fingerprint

Tumors
Genes
Neoplasm Genes
Neoplasms
Prostatic Neoplasms
Gene Regulatory Networks
Microarray Analysis
Learning
Microarrays
Tissue

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Health Informatics

Cite this

Shin, Eunji ; Yoon, Youngmi ; Ahn, Jaegyoon ; Park, Sanghyun. / TC-VGC : A Tumor Classification System using Variations in Genes' Correlation. In: Computer Methods and Programs in Biomedicine. 2011 ; Vol. 104, No. 3. pp. e87-e101.
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TC-VGC : A Tumor Classification System using Variations in Genes' Correlation. / Shin, Eunji; Yoon, Youngmi; Ahn, Jaegyoon; Park, Sanghyun.

In: Computer Methods and Programs in Biomedicine, Vol. 104, No. 3, 01.12.2011, p. e87-e101.

Research output: Contribution to journalArticle

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