Genes Expression Classification Through Histone Modification Using Temporal Neural Network

Background: Genes expression is high dimensional data, so it is very difficult to classify high dimensional data through traditional machine learning approaches. In this work we have proposed a model based on combined approach of Convolutional Neural Network and Recurrent Neural Network, both belong to deep learning model. The prediction has shown improved result than other machine learning algorithms. Expressions are generated through histone modification. Methods: To improve the accuracy deep learning model is proposed i.e. based on Convolutional and Recurrent neural network. This proposed model uses filter, causal convolutional layers and Residual Block for predictions. Results: In this work we have implemented the machine learning algorithms and deep learning algorithms like Logistic Regression, SVM, CNN, Deep Chrome and the proposed Temporal Neural Network. The performance is measured on the basis of parameters like accuracy, precision and AUC on the training and testing set. Conclusion: The proposed Temporal Neural Network model has shown better performance than other machine learning and deep learning algorithms. Due to this proposed deep learning algorithm can be successfully applied on the genes expression dataset.