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Network Inference in Molecular Biology A Hands-on Framework /
Inferring gene regulatory networks is a difficult problem to solve due to the relative scarcity of data compared to the potential size of the networks. While researchers have developed techniques to find some of the underlying network structure, there is still no one-size-fits-all algorithm for ever...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer New York : Imprint: Springer,
2012.
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| Edición: | 1st ed. 2012. |
| Colección: | SpringerBriefs in Electrical and Computer Engineering,
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 001 | 978-1-4614-3113-8 | ||
| 003 | DE-He213 | ||
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| 024 | 7 | |a 10.1007/978-1-4614-3113-8 |2 doi | |
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| 100 | 1 | |a Lingeman, Jesse M. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Network Inference in Molecular Biology |h [electronic resource] : |b A Hands-on Framework / |c by Jesse M. Lingeman, Dennis Shasha. |
| 250 | |a 1st ed. 2012. | ||
| 264 | 1 | |a New York, NY : |b Springer New York : |b Imprint: Springer, |c 2012. | |
| 300 | |a IX, 100 p. 58 illus. |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 | ||
| 490 | 1 | |a SpringerBriefs in Electrical and Computer Engineering, |x 2191-8120 | |
| 505 | 0 | |a The Gene Network Inference Problem -- Experimental Inputs -- Running Examples -- Overall Workflow of Inference -- Sample Pipelines -- Roll Your Own Pipeline -- Appendix. | |
| 520 | |a Inferring gene regulatory networks is a difficult problem to solve due to the relative scarcity of data compared to the potential size of the networks. While researchers have developed techniques to find some of the underlying network structure, there is still no one-size-fits-all algorithm for every data set. Network Inference in Molecular Biology examines the current techniques used by researchers, and provides key insights into which algorithms best fit a collection of data. Through a series of in-depth examples, the book also outlines how to mix-and-match algorithms, in order to create one tailored to a specific data situation. Network Inference in Molecular Biology is intended for advanced-level students and researchers as a reference guide. Practitioners and professionals working in a related field will also find this book valuable. | ||
| 650 | 0 | |a Bioinformatics. | |
| 650 | 0 | |a Algorithms. | |
| 650 | 1 | 4 | |a Computational and Systems Biology. |
| 650 | 2 | 4 | |a Bioinformatics. |
| 650 | 2 | 4 | |a Algorithms. |
| 700 | 1 | |a Shasha, Dennis. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9781461431145 |
| 776 | 0 | 8 | |i Printed edition: |z 9781461431121 |
| 830 | 0 | |a SpringerBriefs in Electrical and Computer Engineering, |x 2191-8120 | |
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| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||