Analysis of Single-Cell Data
ODE Constrained Mixture Modeling and Approximate Bayesian Computation
(Sprache: Englisch)
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...
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Klappentext zu „Analysis of Single-Cell Data “
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.
Inhaltsverzeichnis zu „Analysis of Single-Cell Data “
Modeling and Parameter Estimation for Single-CellData.- ODE Constrained Mixture Modeling for Multivariate Data.- ApproximateBayesian Computation Using Multivariate Statistics.
Autoren-Porträt von Carolin Loos
Carolin Loos is currently doing her PhD at the Institute ofComputational Biology at the Helmholtz Zentrum München. She is member of thejunior research group "Data-driven Computational Modeling".
Bibliographische Angaben
- Autor: Carolin Loos
- 2016, 1st ed. 2016, XXI, 92 Seiten, Maße: 14,8 x 21 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3658132337
- ISBN-13: 9783658132330
- Erscheinungsdatum: 23.03.2016
Sprache:
Englisch
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