Classification of Breast Cancer Proteins Using DRNN Method - Rao, B Madhav; Rao, V Srinivasa; Rao, K Srinivasa (International Journal of Computer Sciences and Engineering, 2019)
Classification of large amount data is one of the major difficult tasks in data science. This problem can be solved by using deep learning techniques like CNN and RNN. In computational bio informatics, protein sequence classification plays a crucial role to determine the accuracy. The proposed approach uses the RNN based architecture with GRU, LSTM, and basic LSTM and find the accuracy of training data and testing data by considering mean value of three methods. In this method the top fifteen proteins which are obtained by using preprocessing and sequence analyzer methods as one set of input and TCGA breast cancer dataset as second input to this proposed method. Every sequence in test dataset will compare with sequences in train dataset to get accurate classification results. Supervised learning requires complete labeled data where as unsupervised learning requires unlabelled data. In this approach semi supervised learning is used to get high throughput.