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Deep learning in biology and medicine [electronic resource] / editors, Davide Bacciu, Paulo J.G. Lisboa, Alfredo Vellido.

Contributor(s): Bacciu, Davide | Lisboa, P. J. G. (Paulo J. G.), 1958- | Vellido, Alfredo.
Material type: materialTypeLabelBookPublisher: New Jersey : World Scientific, 2022Description: 1 online resource (332 p.).ISBN: 9781800610941; 1800610947.Subject(s): Medical informatics | Artificial intelligence -- Medical applications | BioinformaticsGenre/Form: Electronic books.DDC classification: 610.285 Online resources: Access to full text is restricted to subscribers.
Contents:
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.
Summary: "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.
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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.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

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