Agent enabled Mining of Distributed Protein Data Banks

Mining biological data is an emergent area at the intersection between bioinformatics and data mining (DM). The intelligent agent based model is a popular approach in constructing Distributed Data Mining (DDM) systems to address scalable mining over large scale distributed data. The nature of associations between different amino acids in proteins has also been a subject of great anxiety. There is a strong need to develop new models and exploit and analyze the available distributed biological data sources. In this study, we have designed and implemented a multi-agent system (MAS) called Agent enriched Quantitative Association Rules Mining for Amino Acids in distributed Protein Data Banks (AeQARM-AAPDB). Such globally strong association rules enhance understanding of protein composition and are desirable for synthesis of artificial proteins. A real protein data bank is used to validate the system.