title: Amino Acid Principal Component Analysis (AAPCA) and its applications in protein structural class prediction.
abstract: The extremely complicated nature of many biological problems makes them bear the features of fuzzy sets, such as with vague, imprecise, noisy, ambiguous, or input-missing information For instance, the current data in classifying protein structural classes are typically a fuzzy set To deal with this kind of problem, the AAPCA (Amino Acid Principal Component Analysis) approach was introduced. In the AAPCA approach the 20-dimensional amino acid composition space is reduced to an orthogonal space with fewer dimensions, and the original base functions are converted into a set of orthogonal and normalized base functions The advantage of such an approach is that it can minimize the random errors and redundant information in protein dataset through a principal component selection, remarkably improving the success rates in predicting protein structural classes It is anticipated that the AAPCA approach can be used to deal with many other classification problems in proteins as well.
authors.affiliation: Tianjin University of Technology and Education, Mathematical Department, Liulin East, Hexi District, Tianjin, 300222, China. duqishi@yahoo.com
authors.forename: Qi-Shi
authors.initials: QS
authors.lastname: Du
authors.forename: Zhi-Qin
authors.initials: ZQ
authors.lastname: Jiang
authors.forename: Wen-Zhang
authors.initials: WZ
authors.lastname: He
authors.forename: Da-Peng
authors.initials: DP
authors.lastname: Li
authors.forename: Kou-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000596:Amino Acids / Q000737:chemistry*; D019295:Computational Biology; D008958:Models, Molecular; D025341:Principal Component Analysis / Q000379:methods*; D011487:Protein Conformation; D011506:Proteins / Q000737:chemistry* / Q000145:classification; D020539:Sequence Analysis, Protein
chemical_list: D000596:Amino Acids; D011506:Proteins