Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)

Algorithm Study Based on Rough Entropy for Gene Analysis and Selection

Authors
Jia-yang Wang1, Zu-jian Wu
1College of Information Science and Engineering, Central South University
Corresponding Author
Jia-yang Wang
Available Online October 2007.
DOI
https://doi.org/10.2991/iske.2007.146How to use a DOI?
Keywords
Rough set, Entropy, Bioinformatics, Gene
Abstract

Gene expression data has been used to analyse and classify disease in resent years. Combining the attribute importance in Rough Sets Theory and entropy in Information Theory, this paper introduces the study of the gene analysis and selection method. A novel algorithm, called RMSME, is proposed to use the minimum uncertain information to reduct and generate the mostly related genes with the subclasses of disease. Finally, the experimental results show the effectiveness and practicalbility of this algorithm on the actual medical data.

Copyright
© 2007, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
Series
Advances in Intelligent Systems Research
Publication Date
October 2007
ISBN
978-90-78677-04-8
ISSN
1951-6851
DOI
https://doi.org/10.2991/iske.2007.146How to use a DOI?
Copyright
© 2007, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jia-yang Wang
AU  - Zu-jian Wu
PY  - 2007/10
DA  - 2007/10
TI  - Algorithm Study Based on Rough Entropy for Gene Analysis and Selection
BT  - Proceedings of the 2007 International Conference on Intelligent Systems and Knowledge Engineering (ISKE 2007)
PB  - Atlantis Press
SP  - 855
EP  - 861
SN  - 1951-6851
UR  - https://doi.org/10.2991/iske.2007.146
DO  - https://doi.org/10.2991/iske.2007.146
ID  - Wang2007/10
ER  -