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＜電子ブック＞
Analysis of Single-Cell Data : ODE Constrained Mixture Modeling and Approximate Bayesian Computation / by Carolin Loos
(BestMasters. ISSN:26253615)

版	1st ed. 2016.
出版者	(Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Spektrum)
出版年	2016
大きさ	XXI, 92 p. 26 illus : online resource
著者標目	*Loos, Carolin author SpringerLink (Online service)
件　名	LCSH:Biomathematics LCSH:Mathematics—Data processing LCSH:Bioinformatics FREE:Mathematical and Computational Biology FREE:Computational Mathematics and Numerical Analysis FREE:Computational and Systems Biology
一般注記	Modeling and Parameter Estimation for Single-Cell Data -- ODE Constrained Mixture Modeling for Multivariate Data -- Approximate Bayesian Computation Using Multivariate Statistics 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“ HTTP:URL=https://doi.org/10.1007/978-3-658-13234-7

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