Location of Repository

Supervised learning method for the prediction of subcellular localization of proteins using amino acid and amino acid pair composition

By Tanwir Habib, Chaoyang Zhang, Jack Y Yang, Mary Qu Yang and Youping Deng
Topics: Research
Publisher: BioMed Central
OAI identifier: oai:pubmedcentral.nih.gov:2386058
Provided by: PubMed Central
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://www.pubmedcentral.nih.g... (external link)
  • Suggested articles

    Preview

    Citations

    1. (1997). Apweiler R: The SWISS-PROT protein sequence data bank and its supplement TrEMBL. Nucleic Acids Res
    2. (1993). C4.5: Programs for Machine Learning.
    3. (1994). Discrimination of intracellular and extracellular proteins using amino-acid-composition and residue-pair frequencies.
    4. (1986). G: A new method for predicting sequence cleavage site. Nucleic Acids Res
    5. Learning by Building Identification Trees”,.
    6. (1986). Learning internal representations by error propagation. Parallel Distributed Processing: Explorations in the microstructure of cognition
    7. (1990). Lipman DJ: Basic local alignment search tool.
    8. (2004). NeC4.5: neural ensemble based C4.5.
    9. (1995). Nédellec C: Working Noted of the
    10. (2003). Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs. Bioinformatics
    11. (1999). Protein subcellular location prediction. Protein Eng
    12. Raghava GP: ESLpred: SVMachine-based method for subcellular localization of eukaryotic proteins using dipeptide compositions and PSI-BLAST. Nucleic Acids Res 2004, 32(Web server issue):W414-W419.
    13. (2005). Raghava GP: Support Vector Machine-based method for subcellular localization of human proteins using amino acid compositions, their order, and similarity search.
    14. (1998). Statistical Learning Theory.
    15. (2001). Sun Z: Support vector machine approach for protein subcellular localization prediction. Bioinformatics
    16. (1995). The nature of Statistical Learning Theory.
    17. (1998). Using neural networks for prediction of the subcellular location of proteins.
    18. (1995). Vapnik V: Support vector networks.
    19. von Heijne G: Locating proteins in the cell using TargetP, SignalP, and related tools.
    20. (1997). von Heijne G: Prediction of N-terminal protein sorting signals. Curr Opin Struct Biol
    21. (1998). Wanted: Subcellular localization of proteins based on sequence.

    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.