Includes bibliographical references and index.

Introduction -- Deep learning for medical imaging -- The evolution of mining electronic health records in the era of deep learning -- Natural language technologies in the biomedical domain -- Metabolically driven latent space learning for gene expression data -- Deep learning in cheminformatics -- Deep learning methods for network biology -- The need for interpretable and explainable deep learning in medicine and healthcare -- Ethical, societal and legal issues in deep learning for healthcare.

"Biology, medicine and bio-chemistry have become data-centric fields for which Deep Learning methods are delivering ground-breaking results. Addressing high impact challenges, Deep Learning in Biology and Medicine provides an accessible and organic collection of Deep Learning essays on bioinformatics and medicine. It caters for a wide readership, ranging from machine learning practitioners and data scientists seeking methodological knowledge to address biomedical applications, to life science specialists in search of a gentle reference for advanced data analytics. With contributions from internationally renowned experts, the book covers foundational methodologies in a wide spectrum of life science applications including electronic health record processing, diagnostic imaging, text processing, as well as omics-data processing. This survey of consolidated problems is complemented by a selection of advanced applications including cheminformatics and biomedical interaction network analysis. A modern and mindful approach to the use of data-driven methodologies in the life sciences also requires careful consideration of the associated societal, ethical, legal and transparency challenges, covered in the concluding chapters of this book"-- Publisher's website.