Statistical comparison of the efficacy of different minimotif filters and filter combinations.

<p>Statistical comparison of the efficacy of different minimotif filters and filter combinations.</p

Sources of genetic interaction data.

<p>Sources of genetic interaction data.</p

Screen Shot of Minimotif Miner 2.4 filter menu.

<p>GI filters were added as part of MnM website 2.4 located under the motif filter pull down section. The options for filtering with ‘GIs’ are outlined with a red box. This filter can be used on its own or in combination with filters. There are also options to check boxes to include or exclude minimotifs with GIs.</p

ROC curve statistics for differ types of minimotifs.

<p>ROC curve statistics for differ types of minimotifs.</p

Evaluation of genetic interaction filtering algorithms.

1<p>. DR = discrimination ratio.</p

ROC curves for the GI filter with different types of minimotifs.

<p>ROC curves are generated using R software package with activity and sub-activity as the underlying variables. The binomial curve fit is shown. The areas under the ROC curves are 0.93 for all minimotifs (red lines), 0.95 for binding motifs (blue lines) and 0.87 for phosphorylation minimotifs (orange lines).</p

Variations of multi-filter combinations for each model.

1<p>Alternative filter combinations that were not significantly different than the combination tested in the same row (P<0.05) were found: CF+MF+FS+SP for 4-filter combination in linear regression; CF+MF+FS+SP or CF+MF+PPI+SP for 4-filter combination, and MF+FS+PPI or MF+FS+SP for 3-filter combination in support vector machine; MF+FS+PPI for 3-filter combination in neural network.</p

Dependence of minimotif multi-filter performance on threshold values for the linear regression and neural network models.

<p>Sensitivity, specificity, and accuracy for the linear regression (<b>A</b>) support vector machine (<b>B</b>) and neural network (<b>C</b>) models. Thresholds were selected by picking the best model in the 5-fold cross validation (model 2 of the linear regression and model 3 of the neural network) evaluated using the test dataset.</p

ROC plots comparing linear regression, support vector machine, and neural network multi-filters with, individual CF, MF, PPI, FS, and SP filters.

<p>ROCs are colored orange for linear regression, cyan for support vector machine, cyan dark green for neural network, red for PPI filter, blue for CF filter, green for MF filter, purple for FS filter, and yellow for SP filter.</p

ROC statistics for three minimotif multi-filter models.

<p>ROC statistics for three minimotif multi-filter models.</p