caa a22 4500 463186044 CHVBK 20180406164853.0 cr unu---uuuuu 170326e20070701xx s 000 0 eng 10.1007/s00726-006-0475-y doi (NATIONALLICENCE)springer-10.1007/s00726-006-0475-y Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition [Elektronische Daten] [J.-Y. Shi, S.-W. Zhang, Q. Pan, Y.-M. Cheng, J. Xie] 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. Springer-Verlag, 2007 Keywords: Multi-scale energy - Wavelet transform - Support vector machines - Chou's pseudo amino acid composition - Protein subcellular localizations nationallicence Shi J.-Y College of Automation, Northwestern Polytechnical University, Xi'an, China aut Zhang S.-W College of Automation, Northwestern Polytechnical University, Xi'an, China aut Pan Q. College of Automation, Northwestern Polytechnical University, Xi'an, China aut Cheng Y.-M College of Automation, Northwestern Polytechnical University, Xi'an, China aut Xie J. College of Automation, Northwestern Polytechnical University, Xi'an, China aut Amino Acids Springer-Verlag 33/1(2007-07-01), 69-74 0939-4451 33:1<69 2007 33 726 https://doi.org/10.1007/s00726-006-0475-y text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s00726-006-0475-y text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Shi J.-Y College of Automation, Northwestern Polytechnical University, Xi'an, China aut NATIONALLICENCE 700 1- Zhang S.-W College of Automation, Northwestern Polytechnical University, Xi'an, China aut NATIONALLICENCE 700 1- Pan Q. College of Automation, Northwestern Polytechnical University, Xi'an, China aut NATIONALLICENCE 700 1- Cheng Y.-M College of Automation, Northwestern Polytechnical University, Xi'an, China aut NATIONALLICENCE 700 1- Xie J. College of Automation, Northwestern Polytechnical University, Xi'an, China aut NATIONALLICENCE 773 0- Amino Acids Springer-Verlag 33/1(2007-07-01), 69-74 0939-4451 33:1<69 2007 33 726 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer