TY  - JOUR
T1  - IHDGAP: Deep Learning based Intelligent Human Diseases-Gene Association
Prediction Technique for High Dimensional Human Diseases Data Sets
AU - Sakthivel, N.K. AU - Subasree, S. AU - Gopalan, N.P. 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 21
SP  - 8072
EP  - 8079
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.8072.8079
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.8072.8079
KW  - association mining
KW  -Convolution Neural Network (CNN)
KW  -Gene Hierarchical based Random Forest (G-HRF)
KW  -Intelligent Human Disease Gene Association
Prediction (IHDGAP)
KW  -deep learning
KW  -prediction accuracy
AB  - For decades, more and more experimental researches have collectively indicated that microRNA
(miRNA) could play a vital role in many important biological processes and thus, it also the pathogenesis of
human complex diseases. It is also noticed that the resource and time cost requirement for processing data in
traditional biological method is more expensive and thus, more and more focusing have been paid to the
enhancement of efective and accurate computational mechanisms for predicting potential associations between
diseases. To focus towards this, researchers identified that gene is not responsible for many human diseases
and instead, diseases occur due to interaction of different group of genomes that is responsible for different
diseases. Hence, it is very important to analyze and associate the complete genome sequences and its
associations to understand or predict various possible human diseases. To identify and predict the associations
between diseases, this research work is proposed deep learning based Intelligent Human Diseases-Gene
Association Prediction technique for high dimensional human diseases data sets (IHDGAP). This gene disease
sequences prediction technique is proposed through deep learning method that will predict the association
between the diseases. It employs Convolution Neural Network (CNN) algorithm which contains multiple number
of hidden layers which is helping to predict gene patterns and its associations to predict human diseases. The
proposed model, deep learning based Intelligent Human Diseases-Gene Association Prediction technique
(IHDGAP) is implemented and analyzed carefully in terms of processing time, memory usage/utilization,
accuracy, sensitivity, specificity and Fscore. From the experimental results, it is noticed that the proposed deep
learning mechanism improves the performances of the proposed classifier in terms of accuracy, sensitivity,
specificity and Fscore as compared with our previous model gene signature based Hierarchical Random Forest
(G-HRF). However, it was noticed that the proposed model consumes relatively more memory and processing
time as we use Convolution Neural Network (CNN) to predict gene associations.
ER  -