# Deep Learning in Computational Biology: Advancements, Challenges, and Future Outlook

arXiv: https://arxiv.org/abs/2310.03086 · HTML: https://mlsanity.com/paper/2310.03086/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 116 references that are in the map.
- First posted on arXiv 2 Oct 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Development and evaluation of a deep learning model for protein-ligand binding affinity prediction](https://mlsanity.com/paper/1712.07042/) (2017)
- [Deep Learning in Bioinformatics](https://mlsanity.com/paper/1603.06430/) (2016)
- [Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities](https://mlsanity.com/paper/1807.00123/) (2018)
- [On the Origin of Deep Learning](https://mlsanity.com/paper/1702.07800/) (2017)
- [Deep learning for drug repurposing: methods, databases, and applications](https://mlsanity.com/paper/2202.05145/) (2022)
- [Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks](https://mlsanity.com/paper/2010.11644/) (2020)
- [Using deep learning to reveal the neural code for images in primary visual cortex](https://mlsanity.com/paper/1706.06208/) (2017)
- [Applying Deep Reinforcement Learning to the HP Model for Protein Structure Prediction](https://mlsanity.com/paper/2211.14939/) (2022)
- [Graph Neural Operators for Classification of Spatial Transcriptomics Data](https://mlsanity.com/paper/2302.00658/) (2023)
- [A field guide to cultivating computational biology](https://mlsanity.com/paper/2104.11364/) (2021)

## Built on by (most cited first, 2 of 2)

- [CardioTabNet: A Novel Hybrid Transformer Model for Heart Disease Prediction using Tabular Medical Data](https://mlsanity.com/paper/2503.17664/) (2025)
- [SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution](https://mlsanity.com/paper/2406.09168/) (2024)

## Abstract (arXiv)

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.