Davide Bacciu (editor) - Deep Learning In Biology And Medicine

Editors

Davide Bacciu

University of Pisa, Italy

Paulo J. G. Lisboa

Liverpool John Moores University, UK

Alfredo Vellido

Universitat Politcnica de Catalunya, Spain

Published by

World Scientific Publishing Europe Ltd.

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Library of Congress Cataloging-in-Publication Data

Names: Bacciu, Davide, editor. | Lisboa, P. J. G. (Paulo J. G.), 1958 editor. | Vellido, Alfredo, editor.

Title: Deep learning in biology and medicine / editors, Davide Bacciu, University of Pisa, Italy, Paulo J. G. Lisboa, Liverpool John Moores University, UK, Alfredo Vellido, Universitat Politcnica de Catalunya, Spain.

Description: New Jersey : World Scientific, [2022] | Includes bibliographical references and index.

Identifiers: LCCN 2021036691 | ISBN 9781800610934 (hardcover) | ISBN 9781800610941 (ebook) | ISBN 9781800610958 (ebook other)

Subjects: LCSH: Medical informatics. | Artificial intelligence--Medical applications | Bioinformatics.

Classification: LCC R858 .D43412 2022 | DDC 610.285--dc23

LC record available at https://lccn.loc.gov/2021036691

British Library Cataloguing-in-Publication Data

A catalogue record for this book is available from the British Library.

Copyright 2022 by World Scientific Publishing Europe Ltd.

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2022 World Scientific Publishing Europe Ltd.
https://doi.org/10.1142/9781800610941_fmatter

Current life sciences, including biology, medicine and biochemistry are data-centric research fields for which Deep Learning methods and technologies are delivering groundbreaking performances, becoming crucial to address challenges of high impact for societal welfare and wellbeing.

This book provides an accessible and organic collection of Deep Learning essays concerning the life sciences, with a focus 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.

The book is contributed by internationally renowned experts, covering foundational methodologies for a wide range of life sciences problems, 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 needs careful consideration of the associated societal, ethical, legal and transparency challenges, which are covered in the concluding chapters of this book.

2022 World Scientific Publishing Europe Ltd.
https://doi.org/10.1142/9781800610941_fmatter

Davide Bacciu is an Associate Professor at the Department of Computer Science, University of Pisa, where he heads the Pervasive Artificial Intelligence Laboratory. Previously, he was a Visiting Researcher at the Neural Computation Research Group, Liverpool John Moores University, in 20072008 and at the Cognitive Robotic Systems Laboratory, Orebro University, in 2012. He holds a Ph.D. in Computer Science and Engineering from the IMT Lucca Institute for Advanced Studies, for which he has been awarded the 2009 E.R. Caianiello Prize for the best Italian Ph.D. thesis on neural networks. He has co-authored over 120 research works on (deep) neural networks, generative learning, Bayesian models, learning for graphs, continual learning, and distributed and embedded learning systems. He has been the coordinator of several European, national and industrial research projects. Currently, he is Secretary and Board Member of the Italian Association for Artificial Intelligence, a Senior Member of the IEEE and a member of the IEEE CIS Neural Networks Technical Committee. He is the Associate Editor of the IEEE Transactions on Neural Networks and Learning Systems. He chairs the IEEE CIS Task Force on Learning for Structured Data and the Bioinformatics workgroup of the CLAIRE COVID-19 initiative.

Paulo J. G. Lisboa is the Professor and co-Director of the School of Computer Science and Mathematics at Liverpool John Moores University. He is past Chair of the Horizon 2020 Advisory Group for Societal Challenge 1: Health, Demographic Change and Wellbeing, the worlds largest coordinated research programme in health, and of the Healthcare Technologies Professional Network and JA Lodge Prize Committee in the Institution of Engineering and Technology. He is a long-time advocate of interpretable machine learning with over 250 peer-reviewed publications. In 1992, he edited the first book on applications of neural networks. He studied mathematical physics at Liverpool University where he took a Ph.D. in particle physics in 1983. He was appointed Chair of Industrial Mathematics at Liverpool John Moores University in 1996, becoming Head of Graduate School and Head of Department of Applied Mathematics.

Alfredo Vellido is an Associate Professor and former Ramn y Cajal fellow at the Department of Computer Science, Universitat Politcnica de Catalunya (UPC BarcelonaTech) in Barcelona, Spain. Currently coordinator of the Health, Wellbeing and Inclusion area of the Intelligent Data Science and Artificial Intelligence (IDEAI-UPC) Research Center and Chair of the Task Force on Medical Data Analysis for the IEEE-Computational Intelligence Society Data Mining and Big Data Analytics Technical Committee. He is also a member of the CIBER-BBN Spanish network and the Big Data, Inteligencia Artificial (BIGSEN) Group of the Spanish Nephrology Society. He was awarded a Ph.D. in neural computation from Liverpool John Moores University (Liverpool, UK) in 2000. He has devoted a good share of the last 25 years to research in medical applications of machine learning.