Deep Learning Prediction of Exonic Sequence

One of the fundamental pillars of medicine is that decision-making is based on the integration of multidimensional data and complex decision-making. Cancer presents a unique environment for medical decisions because of its diverse forms and progression. Despite advances in technology, reliable identification, characterization, and monitoring of malignancies remain a challenge. The pieces of a gene sequence can reveal details about the types of gene mutations. The exons of the prostrate genes were obtained. The DNA sequence is encoded using k-mer in a deep neural network (DNN) and bidirectional long-short-term memory (Bi-LSTM) model, and the class label is encoded using one-hot encoding. Various categorization criteria were used to evaluate the models. Our experimental findings show that the DNN model prediction achieves 96 percent testing accuracy and 99 percent training accuracy. Training accuracy for the Bi-LSTM model is 95%, and testing accuracy is 91%. Biotechnologists and doctors now have more options thanks to the quick developments in genomic technology. The development of life science technology presents new difficulties. The use of various deep learning models in bioinformatics and medicine presents a number of challenges. Furthermore, the multidisciplinary approach has resulted in the creation of improved models for prediction tasks.