04073nam a22005535i 4500 978-3-031-74748-9 DE-He213 20251030113306.0 cr nn 008mamaa 241110s2024 sz | s |||| 0|eng d 9783031747489 978-3-031-74748-9 10.1007/978-3-031-74748-9 doi QH323.5 PBT bicssc MED090000 bisacsh PBT thema Lecca, Paola. author. aut http://id.loc.gov/vocabulary/relators/aut 87301 Identifiability and Regression Analysis of Biological Systems Models [electronic resource] : Statistical and Mathematical Foundations and R Scripts / by Paola Lecca. 2nd ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XII, 124 p. 26 illus., 16 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda SpringerBriefs in Statistics, 2191-5458 - 1. Complex Systems, Data and Inference -- 2. Dynamic Models -- 3. Model Identifiability -- 4. Regression and Variable Selection -- 5. Parameter Estimation using Artificial Intelligence -- 6. R Scripts. This richly illustrated book presents the latest techniques for the identifiability analysis and standard and robust regression analysis of complex dynamical models, and looks at their objectives. It begins by providing a definition of complexity in dynamic systems, introducing the concepts of system size, density of interactions, stiff dynamics, and the hybrid nature of determination. The discussion then turns to the mathematical foundations of model structural and practical identifiability analysis, multilinear and non-linear regression analysis, and best predictor selection, and their algorithmic procedures. Although the featured examples mainly focus on applications to biochemistry and systems biology, the methodologies described can also be employed in other disciplines such as physics and the environmental sciences. Readers will learn how to determine identifiability conditions, how to search for an identifiable model, and how to conduct their own regression analysis and diagnostics without supervision. This new edition includes a concise, yet comprehensive treatment of the main artificial intelligence methods which can be used for parameter inference in models of complex dynamic biological systems. It emphasizes the most efficient solutions for generating synthetic data that augment the training data and which are indispensable for machine learning procedures. Featuring a wealth of real-world examples, exercises, and R codes, the book addresses the needs of doctoral students and researchers in bioinformatics, bioengineering, systems biology, biophysics, biochemistry, the environmental sciences and experimental physics. Familiarity with the fundamentals of probability and statistics (as provided in first-year university courses) and a basic grasp of R are assumed. Biometry. 65981 Bioinformatics. 64050 Statistics. 65982 Biomathematics. 70325 Biostatistics. 65985 Computational and Systems Biology. 65974 Statistical Theory and Methods. Mathematical and Computational Biology. 70327 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031747472 Printed edition: 9783031747496 SpringerBriefs in Statistics, 2191-5458 84859 https://doi.org/10.1007/978-3-031-74748-9 ZDB-2-SMA ZDB-2-SXMS 64851 64851