Advance Genome Disorder Prediction Model Empowered With Deep Learning

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 explanatory-based approaches were introduced With development technology, genetic data improved cover almost protein then machine deep learning-based Parallel learning introduced. many types conducted on disorder prediction using supervised, unsupervised, semi-supervised techniques, most binary problem sequence data. The results these uncertain because their lower accuracy rate class techniques but not patients’ with his/her history. Most used Ribonucleic acid (RNA) gene often capable handling bid effectively. Consequently, this study, AlexNet as an effective convolutional neural network architecture proposed develop advance model (AGDPM) for predicting multi classes a large amount AGDPM tested compare pre-trained gives best 89.89% & 81.25% training testing respectively. So, shows ability efficiently can process multi-class method. has proved that it single inheritance disorder, mitochondrial multifactorial respect various statistical performance parameters. help will be terms put control rates.