TY - GEN
T1 - Classification of normal and tumor tissues using geometric representation of gene expression microarray data
AU - Kim, Saejoon
AU - Shin, Donghyuk
N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2007
Y1 - 2007
N2 - Microarray is a fascinating technology that provides us with accurate predictions of the state of biological tissue samples simply based on the expression levels of genes available from it. Of particular interest in the use of microarray technology is the classification of normal and tumor tissues which is vital for accurate diagnosis of the disease of interest. In this paper, we shall make use of geometric representation from graph theory for the classification of normal and tumor tissues of colon and ovary. The accuracy of our geometric representation-based classification algorithm will be shown to be comparable to that of the currently known best classification algorithms for the two datasets. In particular, the presented algorithm will be shown to have the highest classification accuracy when the number of genes used for classification is small.
AB - Microarray is a fascinating technology that provides us with accurate predictions of the state of biological tissue samples simply based on the expression levels of genes available from it. Of particular interest in the use of microarray technology is the classification of normal and tumor tissues which is vital for accurate diagnosis of the disease of interest. In this paper, we shall make use of geometric representation from graph theory for the classification of normal and tumor tissues of colon and ovary. The accuracy of our geometric representation-based classification algorithm will be shown to be comparable to that of the currently known best classification algorithms for the two datasets. In particular, the presented algorithm will be shown to have the highest classification accuracy when the number of genes used for classification is small.
UR - http://www.scopus.com/inward/record.url?scp=37249038546&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=37249038546&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-73729-2_37
DO - 10.1007/978-3-540-73729-2_37
M3 - Conference contribution
AN - SCOPUS:37249038546
SN - 9783540737285
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 393
EP - 402
BT - Modeling Decisions for Artificial Intelligence - 4th International Conference, MDAI 2007, Proceedings
PB - Springer Verlag
T2 - 4th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2007
Y2 - 16 August 2007 through 18 August 2007
ER -