Applications of Deep Learning and Reinforcement Learning to Biological Data

By Mufti Mahmud, M. S. Kaiser, Amir Hussain, Stefano Vassanelli. 2017

707 citations recorded by the source corpus.

Rapid advances in hardware-based technologies during the past decades have opened up new possibilities for life scientists to gather multimodal data in various application domains, such as omics, bioimaging, medical imaging, and (brain/body)–machine interfaces. These have generated novel opportunities for development of dedicated data-intensive machine learning techniques. In particular, recent research in deep learning (DL), reinforcement learning (RL), and their combination (deep RL) promise to revolutionize the future of artificial intelligence. The growth in computational power accompanied by faster and increased data storage, and declining computing costs have already allowed scientists in various fields to apply these techniques on data sets that were previously intractable owing to their size and complexity. This paper provides a comprehensive survey on the application of DL, RL, and deep RL techniques in mining biological data. In addition, we compare the performances of DL techniques when applied to different data sets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.

Subfields: Artificial Intelligence, Biophysics, Materials Chemistry

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. P. Heng
2. Chao Yu
3. Jiming Liu
4. Shamim Nemati
5. Maria Tzelepi
6. Hao Chen
7. Lequan Yu
8. Nikolaos Passalis

In Biophysics

1. Lorenzo Natale
2. Chi-Wing Fu
3. P. Heng
4. Lequan Yu
5. Hao Chen
6. Xiaomeng Li

In Materials Chemistry

1. Xiaodi Huang

Citations in the corpus

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• Probabilistic Knowledge Transfer for Lightweight Deep Representation Learning (2020, background): “D EEP learning (DL) allowed for tackling many difficult problems with great success [1], ranging from challenging computer vision problems [2] to complex reinforcement learning problems [3].”
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• Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, background): “Additionally, RL suffers from instability and divergence when all possible action-reward pairs are not used in a given scenario [102].”
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• Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges (2019, background): “Additionally, RL suffers from instability and divergence when all possible action-reward pairs are not used in a given scenario [117].”
• Deep Reinforcement Learning for Cyber Security (2019, background): “[41], biological data mining [42], and drug design [43].”
• A review of epileptic seizure detection using machine learning classifiers (2020, background): “Applications of machine learning are significantly seen on health and biological data sets for better outcomes [15, 16].”
• Deep Learning for EMG-based Human-Machine Interaction: A Review (2021, background): “[18] review deep learning in health-care applications with biomedical signals, including EMG, EEG (Electroencephalogram), ECG (electrocardiogram), and EOG (electrooculogram).”
• EEG-Based Brain-Computer Interfaces (BCIs): A Survey of Recent Studies on Signal Sensing Technologies and Computational Intelligence Approaches and Their Applications (2020, background): “The main benefits of applying deep learning are reducing the time-consuming preprocessing while featuring engineering steps and capturing high-level symbolic features and imperceptible dependencies simultaneously [143].”
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