Advance Genome Disorder Prediction Model Empowered With Deep Learning

초록

A major and essential issue in biomedical research is to predict genome disorder. Genome disorders cause multivariate diseases like cancer, dementia, diabetes, cystic fibrosis, leigh syndrome, etc. which are causes of high mortality rates around the world. In past, theoretical and explanatory-based approaches were introduced to predict genome disorder. With the development of technology, genetic data were improved to cover almost genome and protein then machine and deep learning-based approaches were introduced to predict genome disorder. Parallel machine and deep learning approaches were introduced. In past, many types of research were conducted on genome disorder prediction using supervised, unsupervised, and semi-supervised learning techniques, most of the approaches using binary problem prediction using genetic sequence data. The prediction results of these approaches were uncertain because of their lower accuracy rate and binary class prediction techniques using genome sequence data but not genome disorder patients' data with his/her history. Most of the techniques used Ribonucleic acid (RNA) gene sequence and were not often capable of handling bid data effectively. Consequently, in this study, the AlexNet as an effective convolutional neural network architecture proposed to develop an advance genome disorder prediction model (AGDPM) for predicting genome multi classes disorder using a large amount of data. AGDPM tested and compare with the pre-trained AlexNet neural network model and AGDPM gives the best results with 89.89% & 81.25% accuracy of training and testing respectively. So, the advance genome disorder prediction model shows the ability to efficiently predict genome disorder and can process a large amount of patients' genome disorder data with a multi-class prediction method. AGDPM has proved that it is capable to predict single gene inheritance disorder, mitochondrial gene inheritance disorder, and multifactorial gene inheritance disorder with respect to various statistical performance parameters. So, with the help of AGDPM biomedical research will be improved in terms to predict genetic disorders and put control on high mortality rates.

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