Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition.
Amino Acids
; 33(1): 69-74, 2007 Jul.
Article
in En
| MEDLINE
| ID: mdl-17235454
ABSTRACT
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.
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Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Computer Simulation
/
Proteins
/
Protein Structure, Quaternary
/
Amino Acids
Type of study:
Prognostic_studies
/
Risk_factors_studies
Language:
En
Journal:
Amino Acids
Journal subject:
BIOQUIMICA
Year:
2007
Document type:
Article
Affiliation country:
China