Opportunities and obstacles for deep learning in biology and medicine

Abstract

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 fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, 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 find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, 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 specific 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 labelled 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.

Keywords: deep learning; genomics; machine learning; precision medicine.

Figure 1.

Neural networks come in many different 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 neighbouring 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 figure was inspired by the Neural Network Zoo by Fjodor Van Veen.

Figure 2.

Deep learning applications, tasks and models based on NLP perspectives.