G-Class: A Divide and Conquer Application for Grid Protein Classification
Helen E. Polychroniadou, Fotis E Psomopoulos, Pericles A. Mitkas · Advances in Databases and Information Systems · 2006
Protein classification has always been one of the major challenges in modern functional proteomics. The presence of motifs in protein chains can make the prediction of the functional behavior of proteins possible. The correlation between protein properties and their motifs is not always obvious, since more than one motif may exist within a protein chain. Due to the complexity of this correlation most data mining algorithms are either non efficient or time consuming. In this paper a data mining methodology that utilizes grid technologies is presented. First, data are split into multiple sets while preserving the original data distribution in each set. Then, multiple models are created by using the data sets as independent training sets. Finally, the models are combined to produce the final classification rules, containing all the previously extracted information. The methodology is tested using various protein and protein class subsets. Results indicate the improved time efficiency of our technique compared to other known data mining algorithms.