Abstract
Extracting inherent valuable knowledge from omics big data remains as a daunting problem in bioinformatics and computational biology. Deep learning, as an emerging branch from machine learning, has exhibited unprecedented performance in quite a few applications from academia and industry. We highlight the difference and similarity in widely utilized models in deep learning studies, through discussing their basic structures, and reviewing diverse applications and disadvantages. We anticipate the work can serve as a meaningful perspective for further development of its theory, algorithm and application in bioinformatic and computational biology.
Highlights
Deep learning is the emerging generation of the artificial intelligence techniques, in machine learning
Since the multi-parameter setting in weights adds to the optimization burden, Recurrent Neural Network (RNN) usually performs worse than Convolutional Neural Network (CNN) in terms of fine-tuning
We comprehensively summarized the basic but essential concepts and methods in deep learning, together with its recent applications in diverse biomedical studies
Summary
Extracting inherent valuable knowledge from omics big data remains as a daunting problem in bioinformatics and computational biology. As an emerging branch from machine learning, has exhibited unprecedented performance in quite a few applications from academia and industry. We highlight the difference and similarity in widely utilized models in deep learning studies, through discussing their basic structures, and reviewing diverse applications and disadvantages. We anticipate the work can serve as a meaningful perspective for further development of its theory, algorithm and application in bioinformatic and computational biology
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