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Gene Network Inference Verification of Methods for Systems Genetics Data /
This book presents recent methods for Systems Genetics (SG) data analysis, applying them to a suite of simulated SG benchmark datasets. Each of the chapter authors received the same datasets to evaluate the performance of their method to better understand which algorithms are most useful for obtaini...
| Clasificación: | Libro Electrónico |
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| Autor Corporativo: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg : Imprint: Springer,
2013.
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| Edición: | 1st ed. 2013. |
| Temas: | |
| Acceso en línea: | Texto Completo |
Tabla de Contenidos:
- Simulation of the Benchmark Datasets
- A Panel of Learning Methods for the Reconstruction of Gene Regulatory Networks in a Systems Genetics Context
- Benchmarking a simple yet effective approach for inferring gene regulatory networks from systems genetics data
- Differential Equation based reverse-engineering algorithms: pros and cons
- Gene regulatory network inference from systems genetics data using tree-based methods
- Extending partially known networks
- Integration of genetic variation as external perturbation to reverse engineer regulatory networks from gene expression data
- Using Simulated Data to Evaluate Bayesian Network Approach for Integrating Diverse Data.