Protein classification using sequential pattern mining.
Protein classification in terms of fold recognition can be employed to determine the structural and functional properties of a newly discovered protein. In this work sequential pattern mining (SPM) is utilized for sequence-based fold recognition. One of the most efficient SPM algorithms, cSPADE, is employed for protein primary structure analysis. Then a classifier uses the extracted sequential patterns for classifying proteins of unknown structure in the appropriate fold category. The proposed methodology exhibited an overall accuracy of 36% in a multi-class problem of 17 candidate categories. The classification performance reaches up to 65% when the three most probable protein folds are considered.
Exarchos
Themis P
TP
Dept. of Comput. Sci., Ioannina Univ., Greece. me01238@cc.uio.gr
Papaloukas
Costas
C
Lampros
Christos
C
Fotiadis
Dimitrios I
DI
D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D030562:Databases, Protein; D006801:Humans; D008390:Markov Chains; D008957:Models, Genetic; D008958:Models, Molecular; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D011487:Protein Conformation; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry*; D020539:Sequence Analysis, Protein
D011506:Proteins