Opportunities and obstacles for deep learning in biology and medicine: 2019 update

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well-suited to solve problems of these elds. We examine applications of deep learning to a variety of biomedical problems—patient classi cation, fundamental biological processes, and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we nd that deep learning has yet to revolutionize biomedicine or de nitively resolve any of the most pressing challenges in the eld, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a speci c neural network’s prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labeled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine. Introduction to deep learning Biology and medicine are rapidly becoming data-intensive. A recent comparison of genomics with social media, online videos, and other data-intensive disciplines suggests that genomics alone will equal or surpass other elds in data generation and analysis within the next decade [1]. The volume and complexity of these data present new opportunities, but also pose new challenges. Automated algorithms that extract meaningful patterns could lead to actionable knowledge and change how we develop treatments, categorize patients, or study diseases, all within privacy-critical environments. The term deep learning has come to refer to a collection of new techniques that, together, have demonstrated breakthrough gains over existing best-in-class machine learning algorithms across several elds. For example, over the past ve years these methods have revolutionized image classi cation and speech recognition due to their exibility and high accuracy [2]. More recently, deep learning algorithms have shown promise in elds as diverse as high-energy physics [3], computational chemistry [4], dermatology [5], and translation among written languages [6]. Across elds, “o -the-shelf” implementations of these algorithms have produced comparable or higher accuracy than previous best-in-class methods that required years of extensive customization, and specialized implementations are now being used at industrial scales. Deep learning approaches grew from research on arti cial neurons, which were rst proposed in 1943 [7] as a model for how the neurons in a biological brain process information. The history of arti cial neural networks— referred to as “neural networks” throughout this article—is interesting in its own right [8]. In neural networks, inputs are fed into the input layer, which feeds into one or more hidden layers, which eventually link to an output layer. A layer consists of a set of nodes, sometimes called “features” or “units,” which are connected via edges to the immediately earlier and the immediately deeper layers. In some special neural network architectures, nodes can connect to themselves with a delay. The nodes of the input layer generally consist of the variables being measured in the dataset of interest—for example, each node could represent the intensity value of a speci c pixel in an image or the expression level of a gene in a speci c transcriptomic experiment. The neural networks used for deep learning have multiple hidden layers. Each layer essentially performs feature construction for the layers before it. The training process used often allows layers deeper in the network to contribute to the re nement of earlier layers. For this reason, these algorithms can automatically engineer features that are suitable for many tasks and customize those features for one or more speci c tasks. Deep learning does many of the same things as more familiar machine learning approaches. In particular, deep learning approaches can be used both in supervised applications—where the goal is to accurately predict one or more labels or outcomes associated with each data point—in the place of regression approaches, as well as in unsupervised, or “exploratory” applications—where the goal is to summarize, explain, or identify interesting patterns in a data set—as a form of clustering. Deep learning methods may in fact combine both of these steps. When su cient data are available and labeled, these methods construct features tuned to a speci c problem and combine those features into a predictor. In fact, if the dataset is “labeled” with binary classes, a simple neural network with no hidden layers and no cycles between units is equivalent to logistic regression if the output layer is a sigmoid (logistic) function of the input layer. Similarly, for continuous outcomes, linear regression can be seen as a single-layer neural network. Thus, in some ways, supervised deep learning approaches can be seen as an extension of regression models that allow for greater exibility and are especially well-suited for modeling non-linear relationships among the input features. Recently, hardware improvements and very large training datasets have allowed these deep learning techniques to surpass other machine learning algorithms for many problems. In a famous and early example, scientists from Google demonstrated that a neural network “discovered” that cats, faces, and pedestrians were important components of online videos [9] without being told to look for them. What if, more generally, deep learning takes advantage of the growth of data in biomedicine to tackle challenges in this eld? Could these algorithms identify the “cats” hidden in our data—the patterns unknown to the researcher—and suggest ways to act on them? In this review, we examine deep learning’s application to biomedical science and discuss the unique challenges that biomedical data pose for deep learning methods. Several important advances make the current surge of work done in this area possible. Easy-to-use software packages have brought the techniques of the eld out of the specialist’s toolkit to a broad community of computational scientists. Additionally, new techniques for fast training have enabled their application to larger datasets [10]. Dropout of nodes, edges, and layers makes networks more robust, even when the number of parameters is very large. Finally, the larger datasets now available are also su cient for tting the many parameters that exist for deep neural networks. The convergence of these factors currently makes deep learning extremely adaptable and capable of addressing the nuanced di erences of each domain to which it is applied. Figure 1: Neural networks come in many di erent forms. Left: a key for the various types of nodes used in neural networks. Simple FFNN: a feed forward neural network in which inputs are connected via some function to an output node and the model is trained to produce some output for a set of inputs. MLP: the multi-layer perceptron is a feed forward neural network in which there is at least one hidden layer between the input and output nodes. CNN: the convolutional neural network is a feed forward neural network in which the inputs are grouped spatially into hidden nodes. In the case of this example, each input node is only connected to hidden nodes alongside their neighboring input node. Autoencoder: a type of MLP in which the neural network is trained to produce an output that matches the input to the network. RNN: a deep recurrent neural network is used to allow the neural network to retain memory over time or sequential inputs. This gure was inspired by the Neural Network Zoo by Fjodor Van Veen. This review discusses recent work in the biomedical domain, and most successful applications select neural network architectures that are well suited to the problem at hand. We sketch out a few simple example architectures in Figure 1. If data have a natural adjacency structure, a convolutional neural network (CNN) can take advantage of that structure by emphasizing local relationships, especially when convolutional layers are used in early layers of the neural network. Other neural network architectures such as autoencoders require no labels and are now regularly used for unsupervised tasks. In this review, we do not exhaustively discuss the di erent types of deep neural network architectures; an overview of the principal terms used herein is given in Table 1. Table 1 also provides select example applications, though in practice each neural network architecture has been broadly applied across multiple types of biomedical data. A recent book from Goodfellow et al. covers neural network architectures in detail [11], and LeCun et al. provide a more general introduction [2].