Noise-robust soft clustering of gene expression time-course data
Citations
3,404 citations
Cites methods from "Noise-robust soft clustering of gen..."
...The transformed profiles were then clustered using the Mfuzz toolbox (Futschik and Carlisle, 2005), which is based on the open-source statistical language R (RDC Team, 2006)....
[...]
...For our analysis, the optimal values of c and m were derived by the iterative refinement procedure as described in Futschik and Carlisle (2005)....
[...]
...tered using the Mfuzz toolbox (Futschik and Carlisle, 2005), which is based on the open-source statistical language R (RDC Team, 2006)....
[...]
828 citations
538 citations
450 citations
Cites methods from "Noise-robust soft clustering of gen..."
...The resulting transformed ratios were standardized by dividing the peptide SILAC ratio for each time point by the SD for this peptide and then subjecting to unsupervised clustering with the fuzzy c-means algorithm as implemented in the Mfuzz package (45, 66) with a fuzzification parameter of 2 and 10 centers....
[...]
424 citations
References
16,371 citations
15,662 citations
"Noise-robust soft clustering of gen..." refers methods in this paper
...For m → 1, it can be shown that the clustering becomes hard.(9) The FCM algorithm is then equivalent to the k -means clustering....
[...]
...Soft clustering can be implemented using algorithms (such as fuzzy c-means) based on minimization of objective functions.(9,10) Alternatively, probabilistic approaches such Gaussian mixture models combined with expectation-maximization schemes can be applied....
[...]
...Several methods for minimizing the objective function Jm have been proposed.(9,10) Fuzzy c-means (FCM) clustering is the most common algorithm for solving this problem....
[...]
9,439 citations
"Noise-robust soft clustering of gen..." refers background in this paper
...It has been widely used in numerous fields of scientific research.(1) Clustering can be especially useful if prior knowledge is little or non-existent, since it requires minimal prior assumptions....
[...]