Name
Abstract | ||
|---|---|---|
In this paper, we have explored the use of improved non – negative matrix factorization (INMF) to analyze gene expression data. Firstly, the mathematical principle of INMF algorithm is analyzed; Secondly, we proposed an INMF - based method for clustering periodic genes, which can provide valuable information for gene network research. Using simulated data, our approach is able to extract periodic genes subsets even when the signal-to-noise ratio is low. Subsequently, our approach is tested by real gene expression datasets from Yeast and is compared with the related other approaches. Our results showed that our scheme is feasible and effective. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11816102_45 | ICIC |
Keywords | Field | DocType |
improved non,periodic gene,clustering gene expression data,gene expression data,inmf algorithm,real gene expression datasets,simulated data,signal-to-noise ratio,mathematical principle,negative matrix factorization,gene network research,non negative matrix factorization,signal to noise ratio,gene expression,gene network | Gene,Pattern recognition,Computer science,Matrix decomposition,Gene expression,Artificial intelligence,Independent component analysis,Cluster analysis,Gene regulatory network,Periodic graph (geometry),Machine learning | Conference |
Volume | ISSN | ISBN |
4115 | 0302-9743 | 3-540-37277-6 |
Citations | PageRank | References |
0 | 0.34 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nini Rao | 1 | 85 | 11.36 |
| Simon J. Shepherd | 2 | 60 | 10.53 |