Across fields, ‘off-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.ĭeep learning approaches grew from research on artificial neurons, which were first proposed in 1943 as a model for how the neurons in a biological brain process information. More recently, deep learning algorithms have shown promise in fields as diverse as high-energy physics, computational chemistry, dermatology and translation among written languages. For example, over the past 5 years, these methods have revolutionized image classification and speech recognition due to their flexibility and high accuracy. 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 fields. 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 volume and complexity of these data present new opportunities, but also pose new challenges.
A recent comparison of genomics with social media, online videos and other data-intensive disciplines suggests that genomics alone will equal or surpass other fields in data generation and analysis within the next decade. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.īiology and medicine are rapidly becoming data-intensive. 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. 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. 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. 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. 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. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. These algorithms have recently shown impressive results across a variety of domains. Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features.
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