TSSub: eukaryotic protein subcellular localization by extracting features from profiles.
This paper introduces a new subcellular localization system (TSSub) for eukaryotic proteins. This system extracts features from both profiles and amino acid sequences. Four different features are extracted from profiles by four probabilistic neural network (PNN) classifiers, respectively (the amino acid composition from whole profiles; the amino acid composition from the N-terminus of profiles; the dipeptide composition from whole profiles and the amino acid composition from fragments of profiles). In addition, a support vector machine (SVM) classifier is added to implement the residue-couple feature extracted from amino acid sequences. The results from the five classifiers are fused by an additional SVM classifier. The overall accuracies of this TSSub reach 93.0 and 77.4% on Reinhardt and Hubbard's eukaryotic protein dataset and Huang and Li's eukaryotic protein dataset, respectively. The comparison with existing methods results shows TSSub provides better prediction performance than existing methods.


BACKGROUND
The web server is available from http://166.111.24.5/webtools/TSSub/index.html.
Guo
Jian
J
Laboratory of Statistical Computation & Bioinformatics, Department of Mathematical Sciences, Tsinghua University Beijing 100084, China. guojian99@tsinghua.org.cn
Lin
Yuanlie
Y
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D016000:Cluster Analysis; D005057:Eukaryotic Cells / Q000378:metabolism*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020543:Proteome / Q000378:metabolism*; D016415:Sequence Alignment / Q000379:methods; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software; D013347:Subcellular Fractions / Q000378:metabolism*
D020543:Proteome