title: Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition.
abstract: Membrane proteins are vitally important for many biological processes and have become an attractive target for both basic research and drug design. Knowledge of membrane protein types often provides useful clues in deducing the functions of uncharacterized membrane proteins. With the unprecedented increasing of newly found protein sequences in the post-genomic era, it is highly demanded to develop an automated method for fast and accurately identifying the types of membrane proteins according to their amino acid sequences. Although quite a few identifiers have been developed in this regard through various approaches, such as covariant discriminant (CD), support vector machine (SVM), artificial neural network (ANN), and K-nearest neighbor (KNN), classifier the way they operate the identification is basically individual. As is well known, wise persons usually take into account the opinions from several experts rather than rely on only one when they are making critical decisions. Likewise, a sophisticated identifier should be trained by several different modes. In view of this, based on the frame of pseudo-amino acid that can incorporate a considerable amount of sequence-order effects, a novel approach called "stacked generalization" or "stacking" has been introduced. Unlike the "bagging" and "boosting" approaches which only combine the classifiers of a same type, the stacking approach can combine several different types of classifiers through a meta-classifier to maximize the generalization accuracy. The results thus obtained were very encouraging. It is anticipated that the stacking approach may also hold a high potential to improve the identification quality for, among many other protein attributes, subcellular location, enzyme family class, protease type, and protein-protein interaction type. The stacked generalization classifier is available as a web-server named "SG-MPt_Pred" at: http://202.120.37.186/bioinf/wangsq/service.htm.
authors.affiliation: Institute of Image Processing & Pattern Recognition, Shanghai Jiaotong University, Shanghai 200240, China. lifesci-sjtu@san.rr.com <lifesci-sjtu@san.rr.com>
authors.forename: Shuang-Quan
authors.initials: SQ
authors.lastname: Wang
authors.forename: Jie
authors.initials: J
authors.lastname: Yang
authors.forename: Kuo-Chen
authors.initials: KC
authors.lastname: Chou
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D000818:Animals; D008565:Membrane Proteins / Q000032:analysis / Q000737:chemistry* / Q000145:classification; D010363:Pattern Recognition, Automated / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods
chemical_list: D008565:Membrane Proteins