Analysis of Single-Cell Data: ODE Constrained Mixture Modeling and Approximate Bayesian Computation (BestMasters) - Softcover
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Synopsis
Carolin Loos introduces two novel approaches for the analysis of single-cell data. Both approaches can be used to study cellular heterogeneity and therefore advance a holistic understanding of biological processes. The first method, ODE constrained mixture modeling, enables the identification of subpopulation structures and sources of variability in single-cell snapshot data. The second method estimates parameters of single-cell time-lapse data using approximate Bayesian computation and is able to exploit the temporal cross-correlation of the data as well as lineage information.
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About the Author
Carolin Loos is currently doing her PhD at the Institute of Computational Biology at the Helmholtz Zentrum München. She is member of the junior research group „Data-driven Computational Modeling“.
From the Back Cover
Carolin Loos introduces two novel approaches for the analysis of single-cell data. Both approaches can be used to study cellular heterogeneity and therefore advance a holistic understanding of biological processes. The first method, ODE constrained mixture modeling, enables the identification of subpopulation structures and sources of variability in single-cell snapshot data. The second method estimates parameters of single-cell time-lapse data using approximate Bayesian computation and is able to exploit the temporal cross-correlation of the data as well as lineage information.
Contents
- Modeling and Parameter Estimation for Single-Cell Data
- ODE Constrained Mixture Modeling for Multivariate Data
- Approximate Bayesian Computation Using Multivariate Statistics
Target Groups
- Researchers and students in the fields of (bio-)mathematics, statistics, bioinformatics
- System biologists, biostatisticians, bioinformaticians
The Author
Carolin Loos is currently doing her PhD at the Institute of Computational Biology at the Helmholtz Zentrum München. She is member of the junior research group „Data-driven Computational Modeling“.
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Analysis of Single-Cell Data: ODE Constrained Mixture Modeling and Approximate Bayesian Computation (BestMasters)
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Carolin Loos introduces two novel approaches for theanalysis of single-cell data. Both approaches can be used to study cellularheterogeneity and therefore advance a holistic understanding of biologicalprocesses. The first method, ODE constrained mixture modeling, enables theidentification of subpopulation structures and sources of variability in single-cellsnapshot data. The second method estimates parameters of single-cell time-lapsedata using approximate Bayesian computation and is able to exploit the temporalcross-correlation of the data as well as lineage information. 116 pp. Englisch. Seller Inventory # 9783658132330
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Publication in the field of natural sciencesCarolin Loos is currently doing her PhD at the Institute ofComputational Biology at the Helmholtz Zentrum Muenchen. She is member of thejunior research group Data-driven Computational Modeling . . Seller Inventory # 117442043
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Taschenbuch. Condition: Neu. Neuware -Carolin Loos introduces two novel approaches for theanalysis of single-cell data. Both approaches can be used to study cellularheterogeneity and therefore advance a holistic understanding of biologicalprocesses. The first method, ODE constrained mixture modeling, enables theidentification of subpopulation structures and sources of variability in single-cellsnapshot data. The second method estimates parameters of single-cell time-lapsedata using approximate Bayesian computation and is able to exploit the temporalcross-correlation of the data as well as lineage information.Springer Spektrum in Springer Science + Business Media, Tiergartenstr. 15-17, 69121 Heidelberg 116 pp. Englisch. Seller Inventory # 9783658132330
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - Carolin Loos introduces two novel approaches for theanalysis of single-cell data. Both approaches can be used to study cellularheterogeneity and therefore advance a holistic understanding of biologicalprocesses. The first method, ODE constrained mixture modeling, enables theidentification of subpopulation structures and sources of variability in single-cellsnapshot data. The second method estimates parameters of single-cell time-lapsedata using approximate Bayesian computation and is able to exploit the temporalcross-correlation of the data as well as lineage information. Seller Inventory # 9783658132330
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Taschenbuch. Condition: Neu. Analysis of Single-Cell Data | ODE Constrained Mixture Modeling and Approximate Bayesian Computation | Carolin Loos | Taschenbuch | xxi | Englisch | 2016 | Springer Fachmedien Wiesbaden | EAN 9783658132330 | Verantwortliche Person für die EU: Springer Spektrum in Springer Science + Business Media, Tiergartenstr. 15-17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Seller Inventory # 103963614
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