東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Linked to FindBook

Google Book

Amazon

博客來

Identifiability and regression analysis of biological systems models = statistical and mathematical foundations and R scripts /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Identifiability and regression analysis of biological systems models/ by Paola Lecca.
Reminder of title:	statistical and mathematical foundations and R scripts /
Author:	Lecca, Paola.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xii, 124 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	- 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.
Contained By:	Springer Nature eBook
Subject:	Regression analysis. -
Online resource:	https://doi.org/10.1007/978-3-031-74748-9
ISBN:	9783031747489

Identifiability and regression analysis of biological systems models = statistical and mathematical foundations and R scripts /
Lecca, Paola.

Identifiability and regression analysis of biological systems modelsstatistical and mathematical foundations and R scripts /[electronic resource] :by Paola Lecca. - Second edition. - Cham :Springer Nature Switzerland :2024. - xii, 124 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in statistics,2191-5458. - SpringerBriefs in statistics..

- 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.

ISBN: 9783031747489

Standard No.: 10.1007/978-3-031-74748-9doiSubjects--Topical Terms:

529831
Regression analysis.

LC Class. No.: QA278.2

Dewey Class. No.: 519.536

Identifiability and regression analysis of biological systems models = statistical and mathematical foundations and R scripts /
LDR:03146nmm a2200349 a 4500 001 2381099
003 DE-He213
005 20241110115721.0
006 m d
007 cr nn 008maaau
008 250418s2024 sz s 0 eng d
020 $a 9783031747489 $q (electronic bk.)
020 $a 9783031747472 $q (paper)
024 7 $a 10.1007/978-3-031-74748-9 $2 doi
035 $a 978-3-031-74748-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA278.2
072 7 $a PBT $2 bicssc
072 7 $a MED090000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.536 $2 23
090 $a QA278.2 $b .L457 2024
100 1 $a Lecca, Paola. $3 3450267
245 1 0 $a Identifiability and regression analysis of biological systems models $h [electronic resource] : $b statistical and mathematical foundations and R scripts / $c by Paola Lecca.
250 $a Second edition.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a xii, 124 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a SpringerBriefs in statistics, $x 2191-5458
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 $a 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.
650 0 $a Regression analysis. $3 529831
650 0 $a Biochemistry $x Statistical methods. $3 2070255
650 0 $a Systems biology $x Statistical methods. $3 2186938
650 1 4 $a Biostatistics. $3 1002712
650 2 4 $a Computational and Systems Biology. $3 3531279
650 2 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Mathematical and Computational Biology. $3 1566274
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a SpringerBriefs in statistics. $3 1565658
856 4 0 $u https://doi.org/10.1007/978-3-031-74748-9
950 $a Mathematics and Statistics (SpringerNature-11649)