  In this work, we modify the superparamagnetic clustering algorithm (SPC) by
adding an extra weight to the interaction formula that considers which genes
are regulated by the same transcription factor. With this modified algorithm
that we call SPCTF, we analyze Spellman et al. microarray data for cell cycle
genes in yeast, and find clusters with a higher number of elements compared
with those obtained with the SPC algorithm. Some of the incorporated genes by
using SPCFT were not detected at first by Spellman et al. but were later
identified by other studies, whereas several genes still remain unclassified.
The clusters composed by unidentified genes were analyzed with MUSA, the motif
finding using an unsupervised approach algorithm, and this allow us to select
the clusters whose elements contain cell cycle transcription factor binding
sites as clusters worth of further experimental studies because they would
probably lead to new cell cycle genes. Finally, our idea of introducing
available information about transcription factors to optimize the gene
classification could be implemented for other distance-based clustering
algorithms
