Sequence-based protein superfamily classification using computational intelligence techniques: a review.
Protein superfamily classification deals with the problem of predicting the family membership of newly discovered amino acid sequence. Although many trivial alignment methods are already developed by previous researchers, but the present trend demands the application of computational intelligent techniques. As there is an exponential growth in size of biological database, retrieval and inference of essential knowledge in the biological domain become a very cumbersome task. This problem can be easily handled using intelligent techniques due to their ability of tolerance for imprecision, uncertainty, approximate reasoning, and partial truth. This paper discusses the various global and local features extracted from full length protein sequence which are used for the approximation and generalisation of the classifier. The various parameters used for evaluating the performance of the classifiers are also discussed. Therefore, this review article can show right directions to the present researchers to make an improvement over the existing methods.
Swati
S
Vipsita
Santanu Kumar
SK
Rath
D000465:Algorithms; D000595:Amino Acid Sequence; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D011506:Proteins / Q000737:chemistry* / Q000145:classification* / Q000235:genetics; D020539:Sequence Analysis, Protein / Q000379:methods*
D011506:Proteins