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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 |
|---|---|
| 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 |
MARC
| LEADER | 00000nam a22000005i 4500 | ||
|---|---|---|---|
| 001 | 978-3-642-45161-4 | ||
| 003 | DE-He213 | ||
| 005 | 20220116160444.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 140103s2013 gw | s |||| 0|eng d | ||
| 020 | |a 9783642451614 |9 978-3-642-45161-4 | ||
| 024 | 7 | |a 10.1007/978-3-642-45161-4 |2 doi | |
| 050 | 4 | |a QH324.2-324.25 | |
| 072 | 7 | |a PS |2 bicssc | |
| 072 | 7 | |a UY |2 bicssc | |
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| 082 | 0 | 4 | |a 570.285 |2 23 |
| 082 | 0 | 4 | |a 570.113 |2 23 |
| 245 | 1 | 0 | |a Gene Network Inference |h [electronic resource] : |b Verification of Methods for Systems Genetics Data / |c edited by Alberto Fuente. |
| 250 | |a 1st ed. 2013. | ||
| 264 | 1 | |a Berlin, Heidelberg : |b Springer Berlin Heidelberg : |b Imprint: Springer, |c 2013. | |
| 300 | |a XI, 130 p. 49 illus., 33 illus. in color. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 505 | 0 | |a 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. | |
| 520 | |a 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 obtaining reliable models from SG datasets. The knowledge gained from this benchmarking study will ultimately allow these algorithms to be used with confidence for SG studies e.g. of complex human diseases or food crop improvement. The book is primarily intended for researchers with a background in the life sciences, not for computer scientists or statisticians. | ||
| 650 | 0 | |a Bioinformatics. | |
| 650 | 0 | |a Molecular genetics. | |
| 650 | 1 | 4 | |a Computational and Systems Biology. |
| 650 | 2 | 4 | |a Bioinformatics. |
| 650 | 2 | 4 | |a Molecular Genetics. |
| 700 | 1 | |a Fuente, Alberto. |e editor. |4 edt |4 http://id.loc.gov/vocabulary/relators/edt | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783642451621 |
| 776 | 0 | 8 | |i Printed edition: |z 9783642451607 |
| 776 | 0 | 8 | |i Printed edition: |z 9783662522042 |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-642-45161-4 |z Texto Completo |
| 912 | |a ZDB-2-SBL | ||
| 912 | |a ZDB-2-SXB | ||
| 950 | |a Biomedical and Life Sciences (SpringerNature-11642) | ||
| 950 | |a Biomedical and Life Sciences (R0) (SpringerNature-43708) | ||