Much progress has been made in the design of rule-based and supervised tools, but they are often genre and task dependent.
We developed an efficient, semi-supervised tool that takes the users' empirical knowledge of cutoff values into account (below which interactions or similarities can be neglected) to combine multiple data networks using an averaging or kernel fusion method (Kittler et al., 1998).
Therefore, we considered BEST a supervised learning tool.
However, as a supervised learning tool, BEST is able to automatically distinguish the two sets of genes using the expression profile of the pre-specified query gene.
OPLS-DA is a supervised analysis tool that was used on three time-course regions (i.e. 0 10 h, 20 50 h, and 80 100 h post elicitation) to reveal differences in the metabolite profiles otherwise masked by PCA using all data points [ 59].
The TIGR Multiexperiment viewer (Tmev version 2.2) [ 24] was used for unsupervised hierarchical clustering (HC) using the average dot product and complete linkage options, the leave-one-out cross-validation, and the supervised statistical tool Significance Analysis of Microarrays (SAM) for identification of discriminant transcripts [ 25] with a false discovery rate set at <1%.
C4.5 decision tree algorithm [48] is extremely useful supervised learning tools in the field of data mining.
Classification or supervised clustering tools include K-nearest neighbor (KNN -classification and the more versatile general classification.
The TIGR MultiExperiment Viewer [ 12] was used for unsupervised hierarchical clustering by applying the average dot product and complete linkage options, and the supervised statistical tools one-class t test with the adjusted Bonferroni correction was used for identification of discriminant transcripts.
Table 1 shows the spectral angle distance (SAD) [3] between the most similar endmembers detected by the original ENVI implementation (using the supervised -dimensional visualization tool to derive the final set of endmembers), the PPI approximation described in Section 3 (implemented in the C++ programming language), and our FPGA-based implementation.
OPLS/O2PLS-DA (orthogonal PLS discriminant analysis) [33], a new supervised classification analysis tool, was used to quantitatively analyze the contribution of each species/phylotype (X variables) to the class separation by creating 2 dummy Y variables, one for GCF samples and another for subgingival PP samples.
