Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition

As more and more genomes have been discovered in recent years, there is an urgent need to develop a reliable method to predict the subcellular localization for the explosion of newly found proteins. However, many well-known prediction methods based on amino acid composition have problems utilizing t...

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Bibliographic Details
Published in:Amino acids 2007-07, Vol.33 (1), p.69-74
Main Authors: Shi, J.-Y, Zhang, S.-W, Pan, Q, Cheng, Y.-M, Xie, J
Format: Article
Language:English
Subjects:
Algorithms
Amino acid composition
Amino acid sequence
Amino acids
Amino Acids - analysis
Chou's pseudo amino acid composition
Composition
Computer Simulation
Databases, Factual
Energy
Feature extraction
Genomes
Information processing
Localization
Mathematical analysis
Multi-scale energy
Position (location)
Predictive Value of Tests
Protein Folding
Protein Structure, Quaternary
Protein subcellular localizations
Proteins
Proteins - chemistry
Proteins - classification
Sequences
Strategy
Support vector machines
Vectors (mathematics)
Wavelet transform
Wavelet transforms
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Summary:As more and more genomes have been discovered in recent years, there is an urgent need to develop a reliable method to predict the subcellular localization for the explosion of newly found proteins. However, many well-known prediction methods based on amino acid composition have problems utilizing the sequence-order information. Here, based on the concept of Chou's pseudo amino acid composition (PseAA), a new feature extraction method, the multi-scale energy (MSE) approach, is introduced to incorporate the sequence-order information. First, a protein sequence was mapped to a digital signal using the amino acid index. Then, by wavelet transform, the mapped signal was broken down into several scales in which the energy factors were calculated and further formed into an MSE feature vector. Following this, combining this MSE feature vector with amino acid composition (AA), we constructed a series of MSEPseAA feature vectors to represent the protein subcellular localization sequences. Finally, according to a new kind of normalization approach, the MSEPseAA feature vectors were normalized to form the improved MSEPseAA vectors, named as IEPseAA. Using the technique of IEPseAA, C-support vector machine (C-SVM) and three multi-class SVMs strategies, quite promising results were obtained, indicating that MSE is quite effective in reflecting the sequence-order effects and might become a useful tool for predicting the other attributes of proteins as well.
ISSN:0939-4451
1438-2199
DOI:10.1007/s00726-006-0475-y