Recurrent Neural Network for Genome Sequencing for Personalized Cancer Treatment in Precision Healthcare

For the purpose of the detection of disease through genome sequencing, the complete genome sequence of the infected individuals is now technically feasible. Identifying all coding exons can be used to identify new diseases that involve disease-related variants and genes. The objective of this paper is to propose deep neural network inspired approach in interpreting genome sequencing with optimization of Long Short Term Memory, LSTM method is in focus for personalized cancer treatment. A distinguishing feature of this proposed work is that it includes optimization technique for LSTM. It is evident from evaluation results that the proposed approach has yielded higher accuracy outcome when compared with conventional hybrid classifiers.