Analysis of Prostate Cancer DNA Sequences Using Bi-direction Long Short Term Memory Model

Abstract

Machine and deep learning-based models are the emerging techniques in addressing prediction problems in biomedical data analysis. DNA sequence prediction is a critical problem that requires huge attention in the biomedical domain. These techniques have been shown to provide better accurate results when compared to the conventional regression-based models. Prediction of the gene sequence that leads to cancerous diseases such as prostate cancer is very crucial. Identifying the most important features in a gene sequence is one of the most challenging tasks and extracting the components of the gene sequence that can give an insight into the kind of mutation in the gene is very important, it will lead to effective drug design and promote the new concept of personalized medicine. In this work we have extracted the exons in the various prostate gene sequence that was used in the experiment, we built a bi-LSTM model using a k-mer encoding for the DNA sequence and one-hot encoding for the class label. The bi-LSTM model was evaluated on different classification metrics. Our experimental results show that the model prediction offers a training accuracy and validation accuracy of 95 percent and 91 percent respectively.