Analysis of Single-Cell Data ODE Constrained Mixture Modeling and Approximate Bayesian Computation /

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 sub...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Loos, Carolin (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Spektrum, 2016.
Edición:1st ed. 2016.
Colección:BestMasters,
Temas:
Biomathematics.
Mathematics > Data processing.
Bioinformatics.
Mathematical and Computational Biology.
Computational Mathematics and Numerical Analysis.
Computational and Systems Biology.
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Analysis of Single-Cell Data  |h [electronic resource] :  |b ODE Constrained Mixture Modeling and Approximate Bayesian Computation /  |c by Carolin Loos. 
250 |a 1st ed. 2016. 
264 1 |a Wiesbaden :  |b Springer Fachmedien Wiesbaden :  |b Imprint: Springer Spektrum,  |c 2016. 
300 |a XXI, 92 p. 26 illus.  |b online resource. 
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505 0 |a Modeling and Parameter Estimation for Single-Cell Data -- ODE Constrained Mixture Modeling for Multivariate Data -- Approximate Bayesian Computation Using Multivariate Statistics. 
520 |a 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". 
650 0 |a Biomathematics. 
650 0 |a Mathematics  |x Data processing. 
650 0 |a Bioinformatics. 
650 1 4 |a Mathematical and Computational Biology. 
650 2 4 |a Computational Mathematics and Numerical Analysis. 
650 2 4 |a Computational and Systems Biology. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer Nature eBook 
776 0 8 |i Printed edition:  |z 9783658132330 
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830 0 |a BestMasters,  |x 2625-3615 
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912 |a ZDB-2-SXMS 
950 |a Mathematics and Statistics (SpringerNature-11649) 
950 |a Mathematics and Statistics (R0) (SpringerNature-43713) 

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