Extraction of knowledge on protein-protein interaction by association rule discovery

• Published in: Bioinformatics (Oxford, England) England), 2002-05, Vol.18 (5), p.705-714
• Main Authors: OYAMA, T, KITANO, K, SATOU, K, ITO, T
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Bacterial diseases; Biological and medical sciences; Databases, Protein; Evaluation Studies as Topic; Experimental bacterial diseases and models; Fundamental and applied biological sciences. Psychology; General aspects; Infectious diseases; Information Storage and Retrieval - methods; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Medical sciences; Protein Binding; Proteins - metabolism; Protozoa; Saccharomyces cerevisiae - chemistry; Saccharomyces cerevisiae - metabolism; Sensitivity and Specificity; Sequence Alignment - methods; Sequence Analysis, Protein - methods; Vaccines, antisera, therapeutical immunoglobulins and monoclonal antibodies
• ISSN: 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/18.5.705

Protein-protein interactions are systematically examined using the yeast two-hybrid method. Consequently, a lot of protein-protein interaction data are currently being accumulated. Nevertheless, general information or knowledge on protein-protein interactions is poorly extracted from these data. Thus we have been trying to extract the knowledge from the protein-protein interaction data using data mining. A data mining method is proposed to discover association rules related to protein-protein interactions. To evaluate the detected rules by the method, a new scoring measure of the rules is introduced. The method allowed us to detect popular interaction rules such as "An SH3 domain binds to a proline-rich region." These results indicate that the method may detect novel knowledge on protein-protein interactions.