@article{ART001508851},
author={정현이 and Youngmi Yoon},
title={Class prediction of an independent sample using a set of gene modules consisting of gene-pairs which were condition(Tumor, Normal) specific.},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2010},
volume={15},
number={12},
pages={197-207}
TY - JOUR
AU - 정현이
AU - Youngmi Yoon
TI - Class prediction of an independent sample using a set of gene modules consisting of gene-pairs which were condition(Tumor, Normal) specific.
JO - Journal of The Korea Society of Computer and Information
PY - 2010
VL - 15
IS - 12
PB - The Korean Society Of Computer And Information
SP - 197
EP - 207
SN - 1598-849X
AB - Using a variety of data-mining methods on high-throughput cDNA microarray data, the level of gene expression in two different tissues can be compared, and DEG(Differentially Expressed Gene) genes in between normal cell and tumor cell can be detected. Diagnosis can be made with these genes, and also treatment strategy can be determined according to the cancer stages. Existing cancer classification methods using machine learning select the marker genes which are differential expressed in normal and tumor samples, and build a classifier using those marker genes. However, in addition to the differences in gene expression levels, the difference in gene-gene correlations between two conditions could be a good marker in disease diagnosis. In this study, we identify gene pairs with a big correlation difference in two sets of samples, build gene classification modules using these gene pairs. This cancer classification method using gene modules achieves higher accuracy than current methods. The implementing clinical kit can be considered since the number of genes in classification module is small. For future study, Authors plan to identify novel cancer-related genes with functionality analysis on the genes in a classification module through GO(Gene Ontology) enrichment validation, and to extend the classification module into gene regulatory networks.
KW - 데이터마이닝(datamining);분류분석(classification);지식기반 데이터마이닝(knowledge-based datamining);마이크로어레이데이터분류분석(microarray data classification)
DO -
UR -
ER -
정현이 and Youngmi Yoon. (2010). Class prediction of an independent sample using a set of gene modules consisting of gene-pairs which were condition(Tumor, Normal) specific.. Journal of The Korea Society of Computer and Information, 15(12), 197-207.
정현이 and Youngmi Yoon. 2010, "Class prediction of an independent sample using a set of gene modules consisting of gene-pairs which were condition(Tumor, Normal) specific.", Journal of The Korea Society of Computer and Information, vol.15, no.12 pp.197-207.
정현이, Youngmi Yoon "Class prediction of an independent sample using a set of gene modules consisting of gene-pairs which were condition(Tumor, Normal) specific." Journal of The Korea Society of Computer and Information 15.12 pp.197-207 (2010) : 197.
정현이, Youngmi Yoon. Class prediction of an independent sample using a set of gene modules consisting of gene-pairs which were condition(Tumor, Normal) specific.. 2010; 15(12), 197-207.
정현이 and Youngmi Yoon. "Class prediction of an independent sample using a set of gene modules consisting of gene-pairs which were condition(Tumor, Normal) specific." Journal of The Korea Society of Computer and Information 15, no.12 (2010) : 197-207.
정현이; Youngmi Yoon. Class prediction of an independent sample using a set of gene modules consisting of gene-pairs which were condition(Tumor, Normal) specific.. Journal of The Korea Society of Computer and Information, 15(12), 197-207.
정현이; Youngmi Yoon. Class prediction of an independent sample using a set of gene modules consisting of gene-pairs which were condition(Tumor, Normal) specific.. Journal of The Korea Society of Computer and Information. 2010; 15(12) 197-207.
정현이, Youngmi Yoon. Class prediction of an independent sample using a set of gene modules consisting of gene-pairs which were condition(Tumor, Normal) specific.. 2010; 15(12), 197-207.
정현이 and Youngmi Yoon. "Class prediction of an independent sample using a set of gene modules consisting of gene-pairs which were condition(Tumor, Normal) specific." Journal of The Korea Society of Computer and Information 15, no.12 (2010) : 197-207.
