Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-71783-6_26 | EvoBIO |
Keywords | Field | DocType |
topological reconstruction,reconstruction method,silico data,transcriptional response,gene regulatory network,sos response,silico model,biological data,linear gene,regulatory network,silico data set,cell cycle,steady state | Data mining,Singular value decomposition,Biological data,Predictability,Data set,Computer science,A priori and a posteriori,Greedy algorithm,Bioinformatics,Gene regulatory network,In silico | Conference |
Volume | ISSN | Citations |
4447 | 0302-9743 | 2 |
PageRank | References | Authors |
0.40 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jochen Supper | 1 | 106 | 8.69 |
| Christian Spieth | 2 | 119 | 12.87 |
| Andreas Zell | 3 | 1419 | 137.58 |