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We focus on finding maximum biclusters with lower mean squared residue and higher row variance.
The central iteration of this approach is a node deletion step that utilizes the means squared residue as the objective function.
From Table 3, the biclusters found by MODPSFLB has a slightly higher squared residue and a higher bicluster size than those by the other three algorithm on both yeast dataset and human dataset.
From Table 3, the biclusters found by DMOIOB has a slightly higher squared residue and a higher bicluster size than those by MOIB on both yeast dataset and human dataset.
Mean squared residue, which measures variability of biclusters based on the arithmetic mean of gene expression, was the first scoring function used to find biclusters [ 8] and it was used in several other biclustering methods subsequently [ 9- 11].
In order to assess the quality of biclusters the algorithm uses the Mean Squared Residue (MSR).
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Therefore the mean squared residue (MSR) of B is defined as the sum of the squared residues to assess overall quality of a bicluster B in Eq. (5).
The best k a / k b then followed from the numerical solution that yielded the lowest sum of squared residues.
The MSR is based on the sum of the squared residues which measure how adequate each expression value is, in comparison with the rest of values of the bicluster (see [ 9] for more details).
Further, the squared mean residue of all the elements in the bicluster is defined as r IJ = ∑ i ∈ I, j ∈ J r 2 ij I × J.
In 2000, Cheng and Church proposed the use of a similarity measure called the mean square residue for identification of coherent biclusters [ 11].
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