A State-of-the-Art Survey on Deep Learning Methods and Applications in Bioinformatics

Abstract

AbstractDeep learning (DL) methods have impacted machine learning-based bioinformatics applications as these methods provide the ability to learn complex non-linear relationships between features. DL methods also allow information leveraging from unlabeled data that does not belong to the problem under study. This paper's main objective is to provide a state-of-the-art survey on deep learning (DL) methods and applications in bioinformatics. Various DL methods, including feed-forward neural networks (FNNs), recurrent neural networks (RNNs), bidirectional recurrent neural networks (BRNNs), and convolutional neural networks (CNN), are presented. Deep learning methods are presented along with a review of the state-of-the-art applications in the bioinformatics domain.KeywordsArtificial intelligenceMachine learningConvolutionalBidirectionalNeuralNetworkGeneticsBioinformatics