EDeepSSP: Explainable deep neural networks for exact splice sites prediction.
Splice site prediction is crucial for understanding underlying gene regulation, gene function for better genome annotation. Many computational methods exist for recognizing the splice sites. Although most of the methods achieve a competent performance, their interpretability remains challenging. Moreover, all traditional machine learning methods manually extract features, which is tedious job. To address these challenges, we propose a deep learning-based approach (EDeepSSP) that employs convolutional neural networks (CNNs) architecture for automatic feature extraction and effectively predicts splice sites. Our model, EDeepSSP, divulges the opaque nature of CNN by extracting significant motifs and explains why these motifs are vital for predicting splice sites. In this study, experiments have been conducted on six benchmark acceptors and donor datasets of humans, cress, and fly. The results show that EDeepSSP has outperformed many state-of-the-art approaches. EDeepSSP achieves the highest area under the receiver operating characteristic curve (AUC_ROC) and area under the precision-recall curve (AUC_PR) of 99.32% and 99.26% on human donor datasets, respectively. We also analyze various filter activities, feature activations, and extracted significant motifs responsible for the splice site prediction. Further, we validate the learned motifs of our model against known motifs of JASPAR splice site database.
['Amilpur|Santhosh|S|0000-0003-1278-2161', 'Bhukya|Raju|R|']
D000818:Animals D017360:Arabidopsis D019295:Computational Biology D000078326:Data Visualization D030541:Databases, Genetic D000077321:Deep Learning D004331:Drosophila melanogaster D006801:Humans D016571:Neural Networks, Computer D022821:RNA Splice Sites D012372:ROC Curve
EDeepSSP Splice site genome annotation convolutional neural network motifs feature activation