Overview
- The first dedicated volume on formal reasoning techniques applied to medical systems, including personalized medicine
- Includes novel contributions on automated reasoning, formal methods, and verification by internationally leading researchers
- Features state-of-the-art research, including chapters on machine learning and artificial intelligence applications
Part of the book series: Computational Biology (COBO, volume 30)
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Table of contents (17 chapters)
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Front Matter
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Model Checking
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Front Matter
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Formal Methods and Logic
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Front Matter
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Stochastic Modelling and Analysis
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Front Matter
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Machine Learning and Artificial Intelligence
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Front Matter
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Keywords
About this book
Over the past 15 years, systems biology and systems medicine have been introduced in an attempt to understand the enormous complexity of life from a computational point of view. This has generated a wealth of new knowledge in the form of computational models, whose staggering complexity makes manual analysis methods infeasible. Sound, trusted, and automated means of analysing the models are thus required in order to be able to trust their conclusions. Above all, this is crucial to engineering safe biomedical devices and to reducing our reliance on wet-lab experiments and clinical trials, which will in turn produce lower economic and societal costs. Some examples of the questions addressed here include: Can we automatically adjust medications for patients with multiple chronic conditions? Can we verify that an artificial pancreas system delivers insulin in a way that ensures Type 1 diabetic patients never suffer from hyperglycaemia or hypoglycaemia? And lastly, can we predict what kind of mutations a cancer cell is likely to undergo?
This book brings together leading researchers from a number of highly interdisciplinary areas, including: · Parameter inference from time series
· Model selection
· Network structure identification
· Machine learning
· Systems medicine
· Hypothesis generation from experimental data
· Systems biology, systems medicine, and digital pathology
· Verification of biomedical devices
“This book presents a comprehensive spectrum of model-focused analysis techniques for biological systems ...an essential resource for tracking the developments of a fast moving field that promises to revolutionize biology and medicine by the automated analysis of models and data.”
Prof Luca Cardelli FRS, University of Oxford
Reviews
Editors and Affiliations
About the editors
Pietro Liò is a Professor of Computational Biology at the Department of Computer Science and Technology at the University of Cambridge, UK. He holds a PhD in Complex Systems and Non Linear Dynamics (University of Firenze, Italy) and a PhD in Genetics (University of Pavia, Italy). His research interests include developing methodologies by integrating bioinformatics, machine learning and modelling approaches. In particular, he is interestedin artificial intelligence/machine learning and computational biology methods for biological and health data, predictive models in personalised and precision medicine, machine learning methods for the integration of multi-scale, multi-omics and multi-physics data, and predictive comorbidity models. He is on the steering committee of Cambridge Big Data, the MPhil in Computational Biology and the UK Virtual Physiological Human.
Bibliographic Information
Book Title: Automated Reasoning for Systems Biology and Medicine
Editors: Pietro Liò, Paolo Zuliani
Series Title: Computational Biology
DOI: https://doi.org/10.1007/978-3-030-17297-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-17296-1Published: 24 June 2019
Softcover ISBN: 978-3-030-17299-2Published: 15 August 2020
eBook ISBN: 978-3-030-17297-8Published: 11 June 2019
Series ISSN: 1568-2684
Series E-ISSN: 2662-2432
Edition Number: 1
Number of Pages: XI, 474
Number of Illustrations: 137 b/w illustrations, 77 illustrations in colour
Topics: Computational Biology/Bioinformatics, Systems Biology, Artificial Intelligence, Health Informatics, Pattern Recognition