Integrative Analysis of Protein Interaction Data

Proceedings of the Twentieth International Conference on Machine Learning, 2000

Volume

Proceedings of the Twentieth International Conference on Machine Learning, 2000

Contents

We have developed a method for the integrative anal-ysis of protein interaction data. It comprises cluster-ing, visualization and data integration components. The method is generally applicable for all sequenced or-ganisms. Here, we describe in detail the combination of protein interaction data in the yeast Saccharomyces cerevisiae with the functional classification of all yeast proteins. We evaluate the utility of the method by com-parison with experimental data and deduce hypotheses about the functional role of so far uncharacterized pro-teins. Further applications of the integrative analysis method are discussed. The method presented here is powerful and flexible. We show that it is capable of mining large-scale data sets.