Abstract
Deep learning, a transformative force in computational biology, has reshaped biological data analysis and interpretation terrain. This review delves into the multifaceted role of deep knowledge in this field, exploring its historical roots, inherent advantages, and persistent challenges. It investigates explicitly its application in two pivotal domains: DNA sequence classification, where it has been used to identify disease-causing mutations, and protein structure prediction from sequence data, where it has enabled the accurate determination of protein tertiary structures. Moreover, it offers a glimpse into the future trajectory of this dynamic field, sparking intrigue and excitement about the potential of deep learning.
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