 Название: Automated Reasoning for Systems Biology and Medicine
Автор: Pietro Lio, Paolo Zuliani
Издательство: Springer
Год: 2019
Страниц: 471
Язык: английский
Формат: pdf (true)
Размер: 16.3 MB
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 (ML) and Artificial Intelligence (AI) applications.
This book presents outstanding contributions in an exciting, new and multidisciplinary research area: the application of formal, automated reasoning techniques to analyse complex models in systems biology and systems medicine. Automated reasoning is a field of computer science devoted to the development of algorithms that yield trustworthy answers, providing a basis of sound logical reasoning. For example, in the semiconductor industry formal verification is instrumental to ensuring that chip designs are free of defects (or “bugs”).
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 (ML)
· Systems medicine
· Hypothesis generation from experimental data
· Systems biology, systems medicine, and digital pathology
· Verification of biomedical devices
The chapters are grouped in four different clusters based on the technique used:
- Model Checking
- Formal Methods and Logic
- Stochastic Modelling and Analysis
- Machine Learning and Artificial Intelligence
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