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.

INTRODUCTION

THE need for novel healthcare solutions and continuous efforts in understating the biological bases of pathologies has pushed extensive research in biological sciences over the last two centuries [1]. Recent technological advancements in life sciences have opened up possibilities not only to study biological systems from a holistic perspective, but provided unprecedented access to molecular details of living organisms [2], [3]. Novel tools for DNA sequencing [4], gene expression (GE) [5], bioimaging [6], neuroimaging [7], and brain–machine interfaces [8] are now available to the scientific community. However, considering the inherent complexity of biological systems together with the high dimensionality, diversity, and noise contaminations, inferring meaningful conclusion from such data is a huge challenge [9]. Therefore, novel instruments are required to process and analyze biological big data that must be robust, reliable, reusable, and accurate [10]. This has encouraged numerous scientists from life and computing sciences disciplines to embark in a multidisciplinary approach to demystify functions and dynamics of living organisms, with remarkable progress reported in biological and biomedical research [11]. Thus, many techniques of artificial intelligence (AI), in particular machine learning (ML), have been proposed over time to facilitate recognition, classification, and prediction of patterns in biological data [12]. Conventional ML techniques can be broadly categorized in two large sets—supervised and unsupervised. The methods pertaining to the supervised learning paradigm classify objects in a pool using a set of known annotations/attributes/features. On the other hand, the unsupervised learning techniques form groups/clusters among the objects in a pool by identifying their similarity, and then use them for classifying the unknowns. Further, the other category, reinforcement learning (RL), allows a system to learn from the experiences it gains through interacting with its environment (see Section II-B for details).