Applications of Deep Learning and Reinforcement Learning to Biological Data

TL;DR

This paper reviews how deep learning and reinforcement learning are transforming biological data analysis across various domains, highlighting recent advances, performance comparisons, and future challenges.

Contribution

It provides a comprehensive survey of DL, RL, and Deep RL applications in biological data mining, including performance assessments and future outlooks.

Findings

Deep learning techniques outperform traditional methods in many biological datasets.

Reinforcement learning shows promise in adaptive biological data analysis.

Integration of DL and RL enhances data interpretation in biomedical applications.

Abstract

Rapid advances of hardware-based technologies during the past decades have opened up new possibilities for Life scientists to gather multimodal data in various application domains (e.g., Omics, Bioimaging, Medical Imaging, and [Brain/Body]-Machine Interfaces), thus generating novel opportunities for development of dedicated data intensive machine learning techniques. Overall, recent research in Deep learning (DL), Reinforcement learning (RL), and their combination (Deep RL) promise to revolutionize 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 datasets that were previously intractable for their size and complexity. This review article provides a comprehensive survey on the application of DL, RL, and Deep RL techniques in mining Biological data. In addition, we compare performances of DL techniques when applied to different datasets across various application domains. Finally, we outline open issues in this challenging research area and discuss future development perspectives.