Biologically, the function of a protein is highly related to its subcellular location. It
is of necessity to develop a reliable method for protein subcellular location prediction,
especially when a large amount of proteins are to be analyzed. Various methods have
been proposed to perform the task. The results, however, are not satisfactory in terms of
effectiveness and efficiency. A hybrid approach combining na?ve Bayesian classifier and
k-nearest neighbor classifier is proposed to classify eukaryotic proteins represented as a
combination of amino acid composition, dipeptide composition, and functional domain
composition. Experimental results show that the total accuracy of a set of 17,655 proteins
can reach up to 91.5%.
Relation:
Journal of Information Science and Engineering 24 (5): 1361-1375
