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.Key Features: oThe first to bring deep learning techniques to the life science communities, whereas other books are focused on bringing life science knowledge to the deep learning communityoAn overview of consolidated approaches and methods as well as an up-to-date overview of the state-of-the-art methodologies and applications of deep learning in biology and medicine. As such, the book is also a useful guide to help navigate the literature, providing a reference for both practitioners and scientistsoThe book also includes several useful references to shared resources, i.e. datasets, code, networks, etc.

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