Agent enabled Mining of Distributed Protein Data Banks

TL;DR

This paper introduces a multi-agent system called AeQARM-AAPDB for mining distributed protein data banks, aiming to discover association rules among amino acids to better understand protein composition.

Contribution

The paper presents a novel multi-agent system for mining distributed biological data, specifically designed to find association rules among amino acids in protein data banks.

Findings

System successfully mined association rules from real protein data

Enhanced understanding of amino acid relationships in proteins

Demonstrated effectiveness on large-scale distributed data

Abstract

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.