  Deep learning has become a powerful tool in computational biology,
revolutionising the analysis and interpretation of biological data over time.
In our article review, we delve into various aspects of deep learning in
computational biology. Specifically, we examine its history, advantages, and
challenges. Our focus is on two primary applications: DNA sequence
classification and prediction, as well as protein structure prediction from
sequence data. Additionally, we provide insights into the outlook for this
field. To fully harness the potential of deep learning in computational
biology, it is crucial to address the challenges that come with it. These
challenges include the requirement for large, labelled datasets and the
interpretability of deep learning models. The use of deep learning in the
analysis of DNA sequences has brought about a significant transformation in the
detection of genomic variants and the analysis of gene expression. This has
greatly contributed to the advancement of personalised medicine and drug
discovery. Convolutional neural networks (CNNs) have been shown to be highly
accurate in predicting genetic variations and gene expression levels. Deep
learning techniques are used for analysing epigenetic data, including DNA
methylation and histone modifications. This provides valuable insights into
metabolic conditions and gene regulation. The field of protein structure
prediction has been significantly impacted by deep learning, which has enabled
accurate determination of the three-dimensional shape of proteins and
prediction of their interactions. The future of deep learning in computational
biology looks promising. With the development of advanced deep learning models
and interpretation techniques, there is potential to overcome current
challenges and further our understanding of biological systems.
