Name
Abstract | ||
|---|---|---|
We discuss a heuristic method for the sparse reconstruction of gene networks. The method is based on iterative greedy algorithms, and uses gene expression data from microarray experiments. Also, we show numerically that the greedy algorithms are able to give good approximative solutions to the sparse reconstruction problem even in the presence of significant levels of noise. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1089/cmb.2007.0185 | JOURNAL OF COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
gene networks inference,combinatorial optimization,statistical mechanics,stochastic processes | Heuristic,Reconstruction problem,Computer science,Sparse approximation,Stochastic process,Combinatorial optimization,Greedy algorithm,Artificial intelligence,Bioinformatics,Gene regulatory network,Machine learning | Journal |
Volume | Issue | ISSN |
15.0 | 1 | 1066-5277 |
Citations | PageRank | References |
5 | 0.50 | 4 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mircea Andrecut | 1 | 73 | 8.52 |
| Stuart Kauffman | 2 | 245 | 32.76 |