語系:
繁體中文
English
說明(常見問題)
圖資館首頁
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Uncertainty in biologya computationa...
~
Geris, Liesbet.
Uncertainty in biologya computational modeling approach /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Uncertainty in biologyedited by Liesbet Geris, David Gomez-Cabrero.
其他題名:
a computational modeling approach /
其他作者:
Geris, Liesbet.
出版者:
Cham :Springer International Publishing :2016.
面頁冊數:
ix, 478 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
標題:
BiologyMathematical models.
電子資源:
http://dx.doi.org/10.1007/978-3-319-21296-8
ISBN:
9783319212968$q(electronic bk.)
Uncertainty in biologya computational modeling approach /
Uncertainty in biology
a computational modeling approach /[electronic resource] :edited by Liesbet Geris, David Gomez-Cabrero. - Cham :Springer International Publishing :2016. - ix, 478 p. :ill., digital ;24 cm. - Studies in mechanobiology, tissue engineering and biomaterials,v.171868-2006 ;. - Studies in mechanobiology, tissue engineering and biomaterials ;v.9..
An Introduction to Uncertainty in the Development of Computational Models of Biological Processes -- Reverse Engineering under Uncertainty -- Probabilistic Computational Causal Discovery for Systems Biology -- Macroscopic Simulation of Individual-Based Stochastic Models for Biological Processes -- The Experimental Side of Parameter Estimation -- Statistical Data Analysis and Modeling -- Optimization in Biology: Parameter Estimation and the Associated Optimization Problem -- Interval Methods -- Model Extension and Model Selection -- Bayesian Model Selection Methods and their Application to Biological ODE Systems -- Sloppiness and the Geometry of Parameter Space -- Modeling and Model Simplification to Facilitate Biological Insights and Predictions -- Sensitivity Analysis by Design of Experiments -- Waves in Spatially-Disordered Neural Fields: a Case Study in Uncertainty Quantification -- X In-silico Models of Trabecular Bone: a Sensitivity Analysis Perspective -- Neuroswarm: a Methodology to Explore the Constraints that Function Imposes on Simulation Parameters in Large-Scale Networks of Biological Neurons -- Prediction Uncertainty Estimation Despite Unidentifiability: an Overview of Recent Developments -- Computational Modeling Under Uncertainty: Challenges and Opportunities.
Computational modeling of biomedical processes is gaining more and more weight in the current research into the etiology of biomedical problems and potential treatment strategies. Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process. This book wants to address four main issues related to the building and validation of computational models of biomedical processes: Modeling establishment under uncertainty Model selection and parameter fitting Sensitivity analysis and model adaptation Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples. This book is intended for graduate students and researchers active in the field of computational modeling of biomedical processes who seek to acquaint themselves with the different ways in which to study the parameter space of their model as well as its overall behavior.
ISBN: 9783319212968$q(electronic bk.)
Standard No.: 10.1007/978-3-319-21296-8doiSubjects--Topical Terms:
206136
Biology
--Mathematical models.
LC Class. No.: QH323.5 / .U53 2016
Dewey Class. No.: 570.15195
Uncertainty in biologya computational modeling approach /
LDR
:03607nmm a2200325 a 4500
001
481558
003
DE-He213
005
20160720155334.0
006
m d
007
cr nn 008maaau
008
161007s2016 gw s 0 eng d
020
$a
9783319212968$q(electronic bk.)
020
$a
9783319212951$q(paper)
024
7
$a
10.1007/978-3-319-21296-8
$2
doi
035
$a
978-3-319-21296-8
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QH323.5
$b
.U53 2016
072
7
$a
MQW
$2
bicssc
072
7
$a
TEC009000
$2
bisacsh
082
0 4
$a
570.15195
$2
23
090
$a
QH323.5
$b
.U54 2016
245
0 0
$a
Uncertainty in biology
$h
[electronic resource] :
$b
a computational modeling approach /
$c
edited by Liesbet Geris, David Gomez-Cabrero.
260
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2016.
300
$a
ix, 478 p. :
$b
ill., digital ;
$c
24 cm.
490
1
$a
Studies in mechanobiology, tissue engineering and biomaterials,
$x
1868-2006 ;
$v
v.17
505
0
$a
An Introduction to Uncertainty in the Development of Computational Models of Biological Processes -- Reverse Engineering under Uncertainty -- Probabilistic Computational Causal Discovery for Systems Biology -- Macroscopic Simulation of Individual-Based Stochastic Models for Biological Processes -- The Experimental Side of Parameter Estimation -- Statistical Data Analysis and Modeling -- Optimization in Biology: Parameter Estimation and the Associated Optimization Problem -- Interval Methods -- Model Extension and Model Selection -- Bayesian Model Selection Methods and their Application to Biological ODE Systems -- Sloppiness and the Geometry of Parameter Space -- Modeling and Model Simplification to Facilitate Biological Insights and Predictions -- Sensitivity Analysis by Design of Experiments -- Waves in Spatially-Disordered Neural Fields: a Case Study in Uncertainty Quantification -- X In-silico Models of Trabecular Bone: a Sensitivity Analysis Perspective -- Neuroswarm: a Methodology to Explore the Constraints that Function Imposes on Simulation Parameters in Large-Scale Networks of Biological Neurons -- Prediction Uncertainty Estimation Despite Unidentifiability: an Overview of Recent Developments -- Computational Modeling Under Uncertainty: Challenges and Opportunities.
520
$a
Computational modeling of biomedical processes is gaining more and more weight in the current research into the etiology of biomedical problems and potential treatment strategies. Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process. This book wants to address four main issues related to the building and validation of computational models of biomedical processes: Modeling establishment under uncertainty Model selection and parameter fitting Sensitivity analysis and model adaptation Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples. This book is intended for graduate students and researchers active in the field of computational modeling of biomedical processes who seek to acquaint themselves with the different ways in which to study the parameter space of their model as well as its overall behavior.
650
0
$a
Biology
$x
Mathematical models.
$3
206136
650
0
$a
Uncertainty
$x
Mathematical models.
$3
263216
650
1 4
$a
Engineering.
$3
210888
650
2 4
$a
Biomedical Engineering.
$3
190464
650
2 4
$a
Computational Science and Engineering.
$3
274685
650
2 4
$a
Computer Appl. in Life Sciences.
$3
274180
700
1
$a
Geris, Liesbet.
$3
737693
700
1
$a
Gomez-Cabrero, David.
$3
737694
710
2
$a
SpringerLink (Online service)
$3
273601
773
0
$t
Springer eBooks
830
0
$a
Studies in mechanobiology, tissue engineering and biomaterials ;
$v
v.9.
$3
559846
856
4 0
$u
http://dx.doi.org/10.1007/978-3-319-21296-8
950
$a
Engineering (Springer-11647)
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000121395
電子館藏
1圖書
電子書
EB QH323.5 U54 2016
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
http://dx.doi.org/10.1007/978-3-319-21296-8
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入