Deep Learning In Biology And Medicine

Biology, medicine and biochemistry have become data-centric fields for which Deep Learning methods are delivering groundbreaking 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 sciences 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, which are covered in the concluding chapters of this book.

Davide Bacciu is Associate Professor at the Department of Computer Science, University of Pisa, where he heads the Pervasive Artificial Intelligence Laboratory. He holds a PhD 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 PhD 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 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 COVID19 initiative.

Paulo J G Lisboa is 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 world's 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 edited the first book on applications of neural networks. He studied mathematical physics at Liverpool University where he took a PhD 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 Ramón y Cajal fellow at the Department of Computer Science, Universitat Politècnica de Catalunya (UPC BarcelonaTech) in Barcelona, Spain. Currently, he is a 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 PhD 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.