Ensemble Learning for Identifying Muscular Dystrophy Diseases Using Codon Bias Pattern

Hereditary traits are anticipated by the mutations in the gene sequences. Identifying a disease based on mutations is an essential and challenging task in the determination of genetic disorders such as Muscular dystrophy. Silent mutation is a single nucleotide variant does not result in changes in the encoded protein but appear in the variation of codon usage pattern that results in disease. A new ensemble learning-based computational model is proposed using the synonymous codon usage for identifying the muscular dystrophy disease. The feature vector is designed by calculating the Relative Synonymous Codon Usage (RSCU) values from the mutated gene sequences and a model is built by adopting codon usage bias pattern. This paper addresses the problem by formulating it as multi-classification trained with feature vectors of fifty-nine RSCU frequency values from the mutated gene sequences. Finally, a model is built based on ensemble learning LibD3C algorithm to recognize muscular dystrophy disease classification. Experiments showed that the accuracy of the classifier shows 90%, which proves that ensemble-based learning, is effective for predicting muscular dystrophy disease.