Welcome to our book review site go-pdf.online!
You may have to Search all our reviewed books and magazines, click the sign up button below to create a free account.
Uncertainty in Biology
Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process. This book wants to address four main issues related to the building and validation of computational models of biomedical processes: 1. Modeling establishment under uncertainty 2. Model selection and parameter fitting 3. Sensitivity analysis and model adaptation 4. Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples. This book is intended for graduate students and researchers active in the field of computational modeling of biomedical processes who seek to acquaint themselves with the different ways in which to study the parameter space of their model as well as its overall behavior.
Between the Lines of Genetic Code
Between the Lines of Genetic Code lays out methodologies and tools for the measurement and evaluation of gene-gene and gene-environment studies and gives perspective on the future of this discipline. The book begins by defining terms for interaction studies, describing methodologies, and critically assessing the viability of current study designs and the possibilities for integrating designs. It then provides recent applications data with case studies in rheumatoid arthritis, multiple sclerosis, myositis and other complex human diseases. Last, it examines current studies and directions for future applications in patient care. Recent multivariate studies show that gene-gene and gene-environme...
Networks of Networks in Biology
Biological systems are extremely complex and have emergent properties that cannot be explained or even predicted by studying their individual parts in isolation. The reductionist approach, although successful in the early days of molecular biology, underestimates this complexity. As the amount of available data grows, so it will become increasingly important to be able to analyse and integrate these large data sets. This book introduces novel approaches and solutions to the Big Data problem in biomedicine, and presents new techniques in the field of graph theory for handling and processing multi-type large data sets. By discussing cutting-edge problems and techniques, researchers from a wide range of fields will be able to gain insights for exploiting big heterogonous data in the life sciences through the concept of 'network of networks'.
Networks of Networks in Biology
Introduces network inspired approaches for the analysis and integration of large, heterogeneous data sets in the life sciences.
Handbook of Statistical Systems Biology
Systems Biology is now entering a mature phase in which the key issues are characterising uncertainty and stochastic effects in mathematical models of biological systems. The area is moving towards a full statistical analysis and probabilistic reasoning over the inferences that can be made from mathematical models. This handbook presents a comprehensive guide to the discipline for practitioners and educators, in providing a full and detailed treatment of these important and emerging subjects. Leading experts in systems biology and statistics have come together to provide insight in to the major ideas in the field, and in particular methods of specifying and fitting models, and estimating the...
Towards Personalized Models of the Cardiovascular System Using 4D Flow MRI
Current diagnostic tools for assessing cardiovascular disease mostly focus on measuring a given biomarker at a specific spatial location where an abnormality is suspected. However, as a result of the dynamic and complex nature of the cardiovascular system, the analysis of isolated biomarkers is generally not sufficient to characterize the pathological mechanisms behind a disease. Model-based approaches that integrate the mechanisms through which different components interact, and present possibilities for system-level analyses, give us a better picture of a patient’s overall health status. One of the main goals of cardiovascular modelling is the development of personalized models based on ...
