LEADER 04461nam 22007815 450 001 9910906293203321 005 20260121155508.0 010 $a9783031747489 010 $a3031747488 024 7 $a10.1007/978-3-031-74748-9 035 $a(MiAaPQ)EBC31773721 035 $a(Au-PeEL)EBL31773721 035 $a(CKB)36538108100041 035 $a(DE-He213)978-3-031-74748-9 035 $a(EXLCZ)9936538108100041 100 $a20241110d2024 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIdentifiability and Regression Analysis of Biological Systems Models $eStatistical and Mathematical Foundations and R Scripts /$fby Paola Lecca 205 $a2nd ed. 2024. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2024. 215 $a1 online resource (130 pages) 225 1 $aSpringerBriefs in Statistics,$x2191-5458 311 08$a9783031747472 311 08$a303174747X 320 $aIncludes bibliographical references and index. 327 $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. 330 $aThis richly illustrated book presents the latest techniques for the identifiability analysis and standard and robust regression analysis of complex dynamical models, and looks at their objectives. It begins by providing a definition of complexity in dynamic systems, introducing the concepts of system size, density of interactions, stiff dynamics, and the hybrid nature of determination. The discussion then turns to the mathematical foundations of model structural and practical identifiability analysis, multilinear and non-linear regression analysis, and best predictor selection, and their algorithmic procedures. Although the featured examples mainly focus on applications to biochemistry and systems biology, the methodologies described can also be employed in other disciplines such as physics and the environmental sciences. Readers will learn how to determine identifiability conditions, how to search for an identifiable model, and how to conduct their own regression analysis and diagnostics without supervision. This new edition includes a concise, yet comprehensive treatment of the main artificial intelligence methods which can be used for parameter inference in models of complex dynamic biological systems. It emphasizes the most efficient solutions for generating synthetic data that augment the training data and which are indispensable for machine learning procedures. Featuring a wealth of real-world examples, exercises, and R codes, the book addresses the needs of doctoral students and researchers in bioinformatics, bioengineering, systems biology, biophysics, biochemistry, the environmental sciences and experimental physics. Familiarity with the fundamentals of probability and statistics (as provided in first-year university courses) and a basic grasp of R are assumed. 410 0$aSpringerBriefs in Statistics,$x2191-5458 606 $aBiometry 606 $aBioinformatics 606 $aStatistics 606 $aBiomathematics 606 $aBiostatistics 606 $aComputational and Systems Biology 606 $aStatistical Theory and Methods 606 $aMathematical and Computational Biology 606 $aBiometria$2thub 606 $aAnàlisi de regressió$2thub 606 $aTaxonomia (Biologia)$2thub 606 $aBioquímica$2thub 606 $aBioinformàtica$2thub 608 $aLlibres electrònics$2thub 615 0$aBiometry. 615 0$aBioinformatics. 615 0$aStatistics. 615 0$aBiomathematics. 615 14$aBiostatistics. 615 24$aComputational and Systems Biology. 615 24$aStatistical Theory and Methods. 615 24$aMathematical and Computational Biology. 615 7$aBiometria 615 7$aAnàlisi de regressió 615 7$aTaxonomia (Biologia) 615 7$aBioquímica 615 7$aBioinformàtica 676 $a572.0727 700 $aLecca$b Paola$f1973-$0985245 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910906293203321 996 $aIdentifiability and Regression Analysis of Biological Systems Models$92251851 997 $aUNINA