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020 _a9783031747489
_9978-3-031-74748-9
024 7 _a10.1007/978-3-031-74748-9
_2doi
050 4 _aQH323.5
072 7 _aPBT
_2bicssc
072 7 _aMED090000
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072 7 _aPBT
_2thema
100 1 _aLecca, Paola.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987301
245 1 0 _aIdentifiability and Regression Analysis of Biological Systems Models
_h[electronic resource] :
_bStatistical and Mathematical Foundations and R Scripts /
_cby Paola Lecca.
250 _a2nd ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXII, 124 p. 26 illus., 16 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Statistics,
_x2191-5458
505 0 _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.
520 _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.
650 0 _aBiometry.
_965981
650 0 _aBioinformatics.
_964050
650 0 _aStatistics.
_965982
650 0 _aBiomathematics.
_970325
650 1 4 _aBiostatistics.
_965985
650 2 4 _aComputational and Systems Biology.
_965974
650 2 4 _aStatistical Theory and Methods.
650 2 4 _aMathematical and Computational Biology.
_970327
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031747472
776 0 8 _iPrinted edition:
_z9783031747496
830 0 _aSpringerBriefs in Statistics,
_x2191-5458
_984859
856 4 0 _uhttps://doi.org/10.1007/978-3-031-74748-9
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
999 _c64851
_d64851