TY  - JOUR
T1  - ECAGS: An Enhanced Cancer-Association based Gene Selection Technique for
Cancer Patterns Classification and Prediction
AU - Subasree, S. AU - Sakthivel, N.K. AU - Gopalan, N.P. 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 21
SP  - 8080
EP  - 8087
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.8080.8087
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.8080.8087
KW  - Bioinformatics
KW  -gene association
KW  -cancer pattern classification
KW  -classification accuracy
KW  -dimensionality reduction
KW  -gene prioritization
AB  - Microarray based Cancer Pattern Classification and Prediction technique is one of the most efficient
mechanisms in Bioinformatics research. This research work studied and analyzed thousands of genes
simultaneously to understand the pattern of the gene expression. This research work focuses to identify and
prioritize genes that are important for gene patterns classification and prediction. This research work proposed
an Enhanced Cancer-Association based Gene Selection technique for Cancer Patterns Classification and
Prediction (ECAGS). The proposed classifier is implemented and studied thoroughly in terms of memory
utilization, execution time (processing time), classification accuracy, sensitivity, specificity and F score. The
experimental results were compared with our previous model called an Enhanced Multi-Objective Particle Swarm
(EMOPS). From our experimental results, it was noticed that the proposed model outperforms our previous
model in terms of memory utilization, execution time (processing time), classification accuracy, sensitivity,
specificity and F score.
ER  -