Zobrazeno 1 - 10
of 18
pro vyhledávání: '"Frank eEmmert-Streib"'
Publikováno v:
Frontiers in Genetics, Vol 7 (2016)
Externí odkaz:
https://doaj.org/article/038236e48d7e4a91988f0eff92fa6a6a
Publikováno v:
Frontiers in Genetics, Vol 7 (2016)
Externí odkaz:
https://doaj.org/article/7db0d1e466f0455191a5f1aaec6cfa4f
Autor:
Frank eEmmert-Streib, Matthias eDehmer
Publikováno v:
Frontiers in Genetics, Vol 6 (2015)
Externí odkaz:
https://doaj.org/article/b931618c3619474dad652f90a4a16f49
Publikováno v:
Frontiers in Genetics, Vol 5 (2014)
In this paper, we shed light on approaches that are currently used to infer networks from gene expression data with respect to their biological meaning. As we will show, the biological interpretation of these networks depends on the chosen theoretica
Externí odkaz:
https://doaj.org/article/8546a773dca24bcb972a75ec623d99ae
Publikováno v:
Frontiers in Cell and Developmental Biology, Vol 2 (2014)
In recent years gene regulatory networks (GRNs) have attracted a lot of interest and many methods have been introduced for their statistical inference from gene expression data. However, despite their popularity, GRNs are widely misunderstood. For th
Externí odkaz:
https://doaj.org/article/665b0a6a63624d18800f55faba9553a0
Publikováno v:
Frontiers in Genetics, Vol 5 (2014)
Externí odkaz:
https://doaj.org/article/314791f731af492387bd730e4c6d0aaa
Autor:
Catharina eOlsen, Gianluca eBontempi, Frank eEmmert-Streib, John eQuackenbush, Benjamin eHaibe-Kains
Publikováno v:
Frontiers in Genetics, Vol 5 (2014)
When inferring networks from high-throughput genomic data, one of the main challenges is the subsequent validation of these networks. In the best case scenario, the true network is partially known from previous research results published in structure
Externí odkaz:
https://doaj.org/article/6c47bb1498814cbcb570458759a5de96
Autor:
Frank eEmmert-Streib
Publikováno v:
Frontiers in Genetics, Vol 5 (2014)
Externí odkaz:
https://doaj.org/article/7991895dffe84cc7b0a5195b3e5ff0d9
Autor:
Frank eEmmert-Streib, Ricardo eDe Matos Simoes, Paul eMullan, Benjamin eHaibe-Kains, Matthias eDehmer
Publikováno v:
Frontiers in Genetics, Vol 5 (2014)
In this study, we infer the breast cancer gene regulatory network from gene expression data. This network is obtained from the application of the BC3Net inference algorithm to a large-scale gene expression data set consisting of $351$ patient samples
Externí odkaz:
https://doaj.org/article/5322c6adb4cf435ab9b92e654700eeed
Publikováno v:
Frontiers in Genetics, Vol 4 (2013)
Despite the development of numerous gene regulatory network (GRN) inference methods in the last years, their application, usage and the biological significance of the resulting GRN remains unclear for our general understanding of large-scale gene exp
Externí odkaz:
https://doaj.org/article/7f43a32e94d74e12a7affdebb86b2173