Fuzzy KNN for predicting membrane protein types from pseudo-amino acid composition.
Cell membranes are vitally important to the life of a cell. Although the basic structure of biological membrane is provided by the lipid bilayer, membrane proteins perform most of the specific functions. Membrane proteins are putatively classified into five different types. Identification of their types is currently an important topic in bioinformatics and proteomics. In this paper, based on the concept of representing protein samples in terms of their pseudo-amino acid composition, the fuzzy K-nearest neighbors (KNN) algorithm has been introduced to predict membrane protein types, and high success rates were observed. It is anticipated that, the current approach, which is based on a branch of fuzzy mathematics and represents a new strategy, may play an important complementary role to the existing methods in this area. The novel approach may also have notable impact on prediction of the other attributes, such as protein structural class, protein subcellular localization, and enzyme family class, among many others.
Shen
Hong-Bin
HB
Institute of Image Processing and Pattern Recognition, Shanghai Jiaotong University, 200030 Shanghai, China.
Yang
Jie
J
Chou
Kuo-Chen
KC
D000465:Algorithms; D000596:Amino Acids / Q000032:analysis*; D017143:Fuzzy Logic; D008565:Membrane Proteins / Q000032:analysis* / Q000145:classification; D008956:Models, Chemical; D020539:Sequence Analysis, Protein / Q000379:methods
D000596:Amino Acids; D008565:Membrane Proteins