Prediction of Subcellular Localization of Eukaryotic Proteins Using Position-Specific Profiles and Neural Network with Weighted Inputs

Journal of Genetics and Genomics

Volumn 34, Issue 12, 2007, Pages 1080-1087

  Zou, Lingyun ^a   Wang, Zhengzhi ^a   Huang, Jiaomin ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

BP neural network; position specific scoring matrices; PSI BLAST; subcellular localization; weighting function

Indexed keywords

CELL PROTEIN;

ACCURACY; ALGORITHM; AMINO ACID SEQUENCE; ARTICLE; ARTIFICIAL NEURAL NETWORK; CELLULAR DISTRIBUTION; EUKARYOTE; MATHEMATICAL ANALYSIS; NONHUMAN; PREDICTION; PROTEIN DATABASE; VALIDATION PROCESS;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DATABASES, PROTEIN; DIPEPTIDES; EUKARYOTIC CELLS; FUZZY LOGIC; INTRACELLULAR SPACE; MARKOV CHAINS; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER); PROTEINS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EUKARYOTA;

EID: 37249082479     PISSN: 16738527     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1673-8527(07)60123-4     Document Type: Article

References (19)

1. Murphy R., Boland M., and Velliste M. Towards a systematics for protein subcellular location: quantitative description of protein location patterns and automated analysis of fluorescence microscope images. Proc Int Conf Intel Sys Mol Biol 8 (2000) 251-259
2. Reinhardt A., and Hubbard T. Using neural networks for prediction of the subcellular location of proteins. Nucleic Acids Res 26 (1998) 2230-2236
3. Chou K., and Elrod D. Protein subcellular location prediction. Protein Eng 12 (1999) 107-118
4. Yuan Z. Prediction of protein subcellular locations using Markov chain models. FEBS Lett 451 (1999) 23-26
5. Hua S., and Sun Z. Support vector machine approach for protein subcellular localization prediction. Bioinformatics 17 (2001) 721-728
6. Huang Y., and Li Y. Prediction of protein subcellular locations using fuzzy kNN method. Bioinformatics 20 (2004) 21-28
7. Emanuelsson O., Nielson H., Brunak S., and von Heijne G. Predicting subcellular localization of proteins based on their N-terminal amino acid sequence. J Mol Biol 300 (2000) 1005-1016
8. Nair R., and Rost B. Inferring subcellular localization through automated lexical analysis. Bioinformatics 18 Suppl 1 (2002) S78-S86
9. Lu Z., Szafron D., Greiner R., Lu P., Wishart D., Poulin B., Anvik J., Macdonell C., and Eisner R. Predicting subcellular localization of proteins using machine-learned classifiers. Bioinformatics 20 (2004) 547-556
10. Altschul S., Madden T., Schaffer A., Zhang Z., Miller W., and Lipman D. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res 25 (1997) 3389-3402
11. Park K., and Kanehisa M. Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs. Bioinformatics 19 (2003) 1656-1663
12. Jones D. Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292 (1999) 195-202
13. Ouali M., and King R. Cascaded multiple classifiers for secondary structure prediction. Protein Sci 9 (2000) 1162-1176
14. Xie D., Li A., Wang M., Fan Z., and Feng H. LOCSVMPSI: a web server for subcellular localization of eukaryotic proteins using SVM and profile of PSI-BLAST. Nucleic Acids Research 33 Web Server issue (2005) W105-W110
15. Holm L. Unification of protein families. Curr Opin Struct Biol 8 (1998) 372-379
16. Oh S., and Lee Y. Sensitivity analysis of single hidden layer neural networks with threshold functions. Neural Networks 6 (1995) 1005-1007
17. Liu Y., You Z., and Cao L. A simple functional neural network for computing the largest and smallest eigenvalues and corresponding eigenvectors of a real symetric matrix. Neurocomputing 67 (2005) 369-383
18. Zou L., and Wang Z. Prediction of non-coding RNA based on neural network with principal component analysis. Journal of Biomedical Engineering Research 26 (2007) 9 (in Chinese with English abstract).
19. Yamashita N., and Fukushima M. On the rate of convergence of the Levenberg-Marquardt method. Computing 15 (2001) 239-249