An ensemble classifier for eukaryotic protein subcellular location prediction using gene ontology categories and amino acid hydrophobicity.
With the rapid increase of protein sequences in the post-genomic age, it is challenging to develop accurate and automated methods for reliably and quickly predicting their subcellular localizations. Till now, many efforts have been tried, but most of which used only a single algorithm. In this paper, we proposed an ensemble classifier of KNN (k-nearest neighbor) and SVM (support vector machine) algorithms to predict the subcellular localization of eukaryotic proteins based on a voting system. The overall prediction accuracies by the one-versus-one strategy are 78.17%, 89.94% and 75.55% for three benchmark datasets of eukaryotic proteins. The improved prediction accuracies reveal that GO annotations and hydrophobicity of amino acids help to predict subcellular locations of eukaryotic proteins.
Department of Orthopedics, Xinqiao Hospital, Third Military Medical University, Chongqing, China.
Liqi
L
Li
Yuan
Y
Zhang
Lingyun
L
Zou
Changqing
C
Li
Bo
B
Yu
Xiaoqi
X
Zheng
Yue
Y
Zhou
D000465:Algorithms; D000596:Amino Acids / Q000378:metabolism*; D030562:Databases, Protein; D005057:Eukaryotic Cells / Q000378:metabolism*; D005796:Genes / Q000235:genetics*; D006801:Humans; D057927:Hydrophobic and Hydrophilic Interactions; D058977:Molecular Sequence Annotation / Q000379:methods*; D011506:Proteins / Q000378:metabolism*; D013347:Subcellular Fractions / Q000378:metabolism; D060388:Support Vector Machine
D000596:Amino Acids; D011506:Proteins