2 options
Analysis of Single-Cell Data : ODE Constrained Mixture Modeling and Approximate Bayesian Computation / by Carolin Loos.
Springer Nature - Springer Mathematics and Statistics eBooks 2016 English International Available online
View online- Format:
- Book
- Author/Creator:
- Loos, Carolin., Author.
- Series:
- BestMasters, 2625-3615
- Language:
- English
- Subjects (All):
- Biomathematics.
- Mathematics--Data processing.
- Mathematics.
- Bioinformatics.
- Mathematical and Computational Biology.
- Computational Mathematics and Numerical Analysis.
- Computational and Systems Biology.
- Local Subjects:
- Mathematical and Computational Biology.
- Computational Mathematics and Numerical Analysis.
- Computational and Systems Biology.
- Physical Description:
- 1 online resource (108 p.)
- Edition:
- 1st ed. 2016.
- Place of Publication:
- Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Spektrum, 2016.
- Language Note:
- English
- Summary:
- 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“.
- Contents:
- Modeling and Parameter Estimation for Single-Cell Data
- ODE Constrained Mixture Modeling for Multivariate Data
- Approximate Bayesian Computation Using Multivariate Statistics.
- Notes:
- Description based upon print version of record.
- Includes bibliographical references.
- ISBN:
- 3-658-13234-5
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.