03690nam a22004695i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001200153072001600165072002300181072001500204100007500219245017300294250001800467264006900485300006500554336002600619337002600645338003600671347002400707490004500731505020800776520180500984650001402789650002002803650001602823650002002839650001902859650003902878650003602917650004402953710003402997773002603031776003603057776003603093830004503129856004603174978-3-031-74748-9DE-He21320251030113306.0cr nn 008mamaa241110s2024    sz |    s    |||| 0|eng d  a97830317474897 a10.1007/978-3-031-74748-92doi 4aQH323.5 7aPBT2bicssc 7aMED0900002bisacsh 7aPBT2thema1 aLecca, Paola.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aIdentifiability and Regression Analysis of Biological Systems Modelsh[electronic resource] :bStatistical and Mathematical Foundations and R Scripts /cby Paola Lecca.  a2nd ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024.  aXII, 124 p. 26 illus., 16 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aSpringerBriefs in Statistics,x2191-54580 a- 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.  aThis 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. 0aBiometry. 0aBioinformatics. 0aStatistics. 0aBiomathematics.14aBiostatistics.24aComputational and Systems Biology.24aStatistical Theory and Methods.24aMathematical and Computational Biology.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303174747208iPrinted edition:z9783031747496 0aSpringerBriefs in Statistics,x2191-545840uhttps://doi.org/10.1007/978-3-031-74748-9