计算机应用与软件2017，Vol.34Issue(2)：251-255,279,6.DOI:10.3969/j.issn.1000-386x.2017.02.045
基于特征融合和有监督局部保持投影的蛋白质亚核定位
PROTEIN SUB-NUCLEAR LOCALIZATION BASED ON FEATURE FUSION AND SUPERVISED LOCALITY PRESERVERVING PROJECTION
摘要
Abstract
The drawbacks of traditional methods of protein sub-nuclear localization are the insufficient information of single feature sequence representations,and the independent relationship between sequence representation and prediction methods.Therefore a fusion representation is constructed by combining pseudo amino acid composition with position specific scoring matrix.From these two single representations,the physical and chemical characteristic information of amino acids and protein evolution information are collected respectively.The low dimensional discriminant features are obtained with the inter-class segmenting and inner-class maintaining characteristics by supervised locality preserving projection learning data low-dimensional manifold.Then depending on the data distribution,nearest neighbor classifier is employed to predict sub-nuclear locations.Finally on the standard data sets,the evaluate results by 10-fold cross validation show that the proposed method has significant improvement in accuracy compared with the existing methods.关键词
融合表达/有监督局部保持投影/最近邻分类器/十折交叉验证Key words
Keywords Fusion representation/Supervised locality preserving projection/K-nearest neighbor classifier/10-fold cross validation分类
信息技术与安全科学引用本文复制引用
刘树慧,王顺芳..基于特征融合和有监督局部保持投影的蛋白质亚核定位[J].计算机应用与软件,2017,34(2):251-255,279,6.基金项目
国家自然科学基金项目(11261068,11661081). （11261068,11661081）
