Netmes: Assessing Gene Network Inference Algorithms by Network-Based Measures
Gene regulatory network inference (GRNI) algorithms are essential for efficiently utilizing large-scale microarray datasets to elucidate biochemical interactions among molecules in a cell. Recently, the combination of network-based error measures complemented with an ensemble approach became popular...
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| Vydáno v: | Evolutionary Bioinformatics Ročník 2014; číslo 2014; s. 1 - 9 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London, England
Libertas Academica
01.01.2014
SAGE Publishing SAGE Publications Sage Publications Ltd. (UK) Sage Publications Ltd |
| Témata: | |
| ISSN: | 1176-9343, 1176-9343 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Gene regulatory network inference (GRNI) algorithms are essential for efficiently utilizing large-scale microarray datasets to elucidate biochemical interactions among molecules in a cell. Recently, the combination of network-based error measures complemented with an ensemble approach became popular for assessing the inference performance of the GRNI algorithms. For this reason, we developed a software package to facilitate the usage of such metrics. In this paper, we present netmes, an R software package that allows the assessment of GRNI algorithms. The software package netmes is available from the R-Forge web site https://r-forge.r-project.org/projects/netmes/. |
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| Bibliografie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 ACADEMIC EDITOR: Jike Cui, Associate Editor |
| ISSN: | 1176-9343 1176-9343 |
| DOI: | 10.4137/EBO.S13481 |