A Mixed Convolution Neural Network for Identifying RNA Pseudouridine sites

Pseudouridine is widely popular among various RNA modifications which has been confirmed to occur in rRNA, mRNA, tRNA, and nuclear/nucleolar RNA. Hence, identifying them has vital significance in academic research, drug development and gene therapies. Several laboratory techniques for Ψ identification have been introduced over the years. Although these techniques produce satisfactory results, they are costly, time consuming and requires skilled experience. As the lengths of RNA sequences are getting longer day by day, an efficient method for identifying pseudouridine sites using computational approach is very important. In this paper, we proposed a mixed convolution neural network using “one-hot” encoding. We employed k-fold cross- validation and grid search to tune the hyperparameters. Our model took care of the feature selection and extraction process automatically. We evaluated its performance in the independent datasets and found promising results. The results proved that our method can be used to identify pseudouridine sites for associated purposes. Our work also projects the increased performance of applying CNN for biological sequences.