A fuzzy logic approach to analyzing gene expression data

We have developed a novel algorithm for analyzing gene expression data. Thi s algorithm uses fuzzy logic to transform expression values into qualitativ e descriptors that can be evaluated by using a set of heuristic rules. In o ur tests we designed a model to find triplets of activators, repressors, an d targets in a yeast gene expression data set. For the conditions tested, t he predictions made by the algorithm agree well with experimental data in t he literature. The algorithm can also assist in determining the function of uncharacterized proteins and is able to detect a substantially larger numb er of transcription factors than could be found at random. This technology extends current techniques such as clustering in that it allows the user to generate a connected network of genes using only expression data.