Novel Method for Mutational Disease Prediction using Bioinformatics Techniques and Backpropagation Algorithm

Abstract

Cancer is one of the most feared diseases in the world it has increased disturbingly and breast cancer occurs in one out of eight women, the prediction of malignancies plays essential roles not only in revealing human genome, but also in discovering effective prevention and treatment of cancers. Generally cancer disease driven by somatic mutations in an individual DNA sequence, or genome that accumulates during the lifetime of person. This paper is proposed a novel method can predict the disease by mutations despite The presence in gene sequence is not necessary it are malignant, so will be compare the protein of patient with the gene's protein of disease if there is difference between these two proteins then can say there is malignant mutations. This method will use bioinformatics techniques like FASTA, CLUSTALW, etc which shows whether malignant mutations or not, then training the backpropagation algorithm using all expected malignant mutations for a certain genes (e.g. BRCA1 and BRCA2) of disease, and using it to test whether patient is holder the disease or not. Implementing this novel method as the first way to predict the disease based on mutations in the sequence of the gene that causes the disease shows two decisions are achieved successfully, the first diagnose whether the patient has mutations of cancer or not using bioinformatics techniques the second classifying these mutations are related to breast cancer (e.g. BRCA1 and BRCA2) using backpropagation with mean square rate 0.0000001. Keywords-Gene sequence; Protein; Deoxyribonucleic Acid DNA; Malignant mutation; Bioinformatics; Back-propagation algorithm.