TY  - JOUR
T1  - Data Driven Approach for Genetic Disorder Prediction by
Aggregating Mutational Features
AU - Kalimuthu, Sathyavikasini AU - Vijayakumar, Vijaya 
JO  - Asian Journal of Information Technology
VL  - 16
IS  - 8
SP  - 675
EP  - 685
PY  - 2017
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2017.675.685
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2017.675.685
KW  - Codon
KW  -classification
KW  -muscular dystrophy
KW  -positional cloning
KW  -pooled features
KW  -RSCU
AB  - In the recent genomic epoch, the recognition of the genetic diseases is paramount. It is a convoluted
task to recognize a heritable disease that is certainly caused by genetic mutations. Identification of disease
based on mutations in the gene sequences is an important and challenging task in the medical diagnosis of
genetic disorders. This study addresses this problem by developing new model by extracting mutational
features as discriminative descriptors for predicting the disease accurately. The disease gene sequences are
mutated by espousing a technique like positional cloning on the reference cDNA sequence. A rare genetic
disorder such as muscular dystrophy is taken as a sample for this research. This disease is a complicated
neuromuscular ailment with a prominent social impact that impairs the working of the locomotive muscle tissues.
The versatile causes of this disease bring about the requirement of new hereditary patterns that can diagnose
patients using biological information. There are diverse significant forms of muscular dystrophy and it is
imperative to identify the type of muscular dystrophy for proper medical diagnosis and medication. Hence, a
data driven model is developed using pattern recognition techniques by aggregating the features related to all
kinds of mutations for predicting the disease precisely. Results indicate that the SVM classifier is found to
acquire the best accuracy of 90.5% for predicting muscular dystrophy.
ER  -