IEEE Trans Neural Netw Learn Syst. 2018 Jun;29(6):2063-2079. doi: 10.1109/TNNLS.2018.2790388.
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.
在过去几十年中，基于硬件的技术的快速发展为生命科学家在各种应用领域（如组学、生物成像、医学成像和（脑/体）机器接口）中收集多模态数据开辟了新的可能性。这些为开发专用的数据密集型机器学习技术提供了新的机会。特别是，深度学习（DL）、强化学习（RL）及其组合（深度 RL）的最新研究有望彻底改变人工智能的未来。计算能力的增长伴随着更快和更多的数据存储以及计算成本的降低，已经使得各个领域的科学家能够应用这些技术来处理以前由于其大小和复杂性而难以处理的数据集。本文对 DL、RL 和深度 RL 技术在挖掘生物数据中的应用进行了全面调查。此外，我们比较了不同应用领域不同数据集上应用 DL 技术的性能。最后，我们概述了这个具有挑战性的研究领域中的开放问题，并讨论了未来的发展前景。
