TY  - JOUR
T1  - An Entropy based Mean Score Feature Selection Method for Identification of Biomarkers using Mirna Expression Profiles for Cancer Classification
AU - Anidha, M. AU - Premalatha, K. 
JO  - Asian Journal of Information Technology
VL  - 16
IS  - 2
SP  - 206
EP  - 211
PY  - 2017
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2017.206.211
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2017.206.211
KW  - Feature selection
KW  -fisher score
KW  -EBMS
KW  -classification
KW  -SVM
KW  -ANN
KW  -10-fold cross validation
AB  - MicroRNAs are small non-coding RNA molecules which are important developments in the cancer biology. miRNA microarrays are useful tools to identify potential biomarkers for variety of cancers. Due to high dimensionality of microarrays, it is very hard to identify cancer oncogenes and classify tumor samples. Feature selection is very essential task in the process of classification and identification of biomarker genes by selecting relevant genes. In this research, Entropy Based Mean Score (EBMS) is employed to identify the biomarker genes in miRNA microarrays. This is based on Fisher score which has the benefits of information gain and achieves maximum classification accuracy. The proposed research is tested on benchmark datasets with SVM and ANN for classification. The experimental results show that the EBMS method outperforms the existing methods and it is suitable for effective feature selection.
ER  -