Reconstructing Linear Gene Regulatory Networks

Autor:	Jochen Supper, Andreas Zell, Christian Spieth
Rok vydání:	2007
Předmět:	Biological data In silico Singular value decomposition Gene regulatory network A priori and a posteriori Data mining Predictability Biology Greedy algorithm computer.software_genre Reconstruction method computer
Zdroj:	Lecture Notes in Computer Science ISBN: 9783540717829 EvoBIO
DOI:	10.1007/978-3-540-71783-6_26
Popis:	The ability to measure the transcriptional response after a stimulus has drawn much attention to the underlying gene regulatory networks. Here, we evaluate the application of methods to reconstruct gene regulatory networks by applying them to the SOS response of E. coli, the budding yeast cell cycle and in silico models. For each network we define an a priori validation network, where each interaction is justified by at least one publication. In addition to the existing methods, we propose a SVD based method (NSS). Overall, most reconstruction methods perform well on in silico data sets, both in terms of topological reconstruction and predictability. For biological data sets the application of reconstruction methods is suitable to predict the expression of genes, whereas the topological reconstruction is only satisfactory with steadystate measurements. Surprisingly, the performance measured on in silico data does not correspond with the performance measured on biological data.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d0bab09075c2d5d190853d7c953e8b0d https://doi.org/10.1007/978-3-540-71783-6_26 Zobrazit plný text záznamu