title: Predicting enzyme subclasses by using support vector machine with composite vectors.
abstract: Based on enzyme sequence, using composite vectors with amino acid composition, low-frequency power spectral density, increment of diversity by combining a different form of pseudo amino acid composition to express the information of sequence, a support vector machine (SVM) for predicting enzyme subclasses is proposed. By the jackknife test, success rates of our algorithm are higher than other methods.
authors: College of Sciences, Inner Mongolia University of Technology, Hohhot, 010051, China.
authors: Ruijia
authors: R
authors: Shi
authors: Xiuzhen
authors: X
authors: Hu
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000704:Analysis of Variance; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D004798:Enzymes / Q000737:chemistry / Q000145:classification* / Q000378:metabolism; D005583:Fourier Analysis; D015203:Reproducibility of Results
chemical_list: D004798:Enzymes