mlsanityData to 14 Oct 2026

Applications of Deep Learning and Reinforcement Learning to Biological Data

arXiv 1711.03985 ↗First posted 10 Nov 2017cs.LG stat.MLcited by 45

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Abstract (arXiv)

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.

Key facts. Cited by 45 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Nov 2017.

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Citing papers per year: 2021: 18; 2022: 12; 2023: 8; 2024: 5; 2025: 1; 2026: 1.

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