The drug discovery process starts with protein
identification because proteins are responsible for many functions
required for maintenance of life. Protein identification further needs
determination of protein function. Proposed method develops a
classifier for human protein function prediction. The model uses
decision tree for classification process. The protein function is
predicted on the basis of matched sequence derived features per each
protein function. The research work includes the development of a
tool which determines sequence derived features by analyzing
different parameters. The other sequence derived features are
determined using various web based tools.
[1] Clare, A. "Machine learning and data mining for yeast functional
genomics", Ph.D. thesis, University of Wales, vol. 11, February, 2003,
pp. 112-183.
[2] Iddo Friedberg "Automated Protein Function Prediction-the genomic
challenge" Briefings in Bioinformatics Vol 7. No. 3., January 2006, pp.
225-242.
[3] L. Jenson "Prediction of Protein Function from Sequence Derived
Protein Features", Ph.D. thesis, Technical University of Denmark, 2002,
pp. 1-570.
[4] D. Krane and RM. Raymer "Fundamental Concepts of Bioinformatics",
Benjamin Cumming, 2006, pp. 1-203.
[5] Page M. J. Page, B. Amess, R. R. Townsend et al. "Proteomic definition
of normal human luminal and myoepithelial breast cells purified from
reduction mammoplasties" Proc. Natl. Acad. Sci. 1999, pp. 12589-
12594.
[6] S. R. Safavian and D. Landgrebe (1991) "A Survey of Decision Tree
Classifier Methodology", IEEE Trans.Systems, Man and Cybernetics,
vol. 21, issue 3, May/June, 1991, pp. 660-674.
[7] T. R. Golub, D. K. Slonim, P. Tamayo et al. "Molecular Classification of
Cancer: Class Discovery and Class Prediction by Gene Expression
Monitoring" Science, Vol. 286. no. 5439, 15 October 1999, pp. 531 -
537.
[1] Clare, A. "Machine learning and data mining for yeast functional
genomics", Ph.D. thesis, University of Wales, vol. 11, February, 2003,
pp. 112-183.
[2] Iddo Friedberg "Automated Protein Function Prediction-the genomic
challenge" Briefings in Bioinformatics Vol 7. No. 3., January 2006, pp.
225-242.
[3] L. Jenson "Prediction of Protein Function from Sequence Derived
Protein Features", Ph.D. thesis, Technical University of Denmark, 2002,
pp. 1-570.
[4] D. Krane and RM. Raymer "Fundamental Concepts of Bioinformatics",
Benjamin Cumming, 2006, pp. 1-203.
[5] Page M. J. Page, B. Amess, R. R. Townsend et al. "Proteomic definition
of normal human luminal and myoepithelial breast cells purified from
reduction mammoplasties" Proc. Natl. Acad. Sci. 1999, pp. 12589-
12594.
[6] S. R. Safavian and D. Landgrebe (1991) "A Survey of Decision Tree
Classifier Methodology", IEEE Trans.Systems, Man and Cybernetics,
vol. 21, issue 3, May/June, 1991, pp. 660-674.
[7] T. R. Golub, D. K. Slonim, P. Tamayo et al. "Molecular Classification of
Cancer: Class Discovery and Class Prediction by Gene Expression
Monitoring" Science, Vol. 286. no. 5439, 15 October 1999, pp. 531 -
537.
@article{"International Journal of Biological, Life and Agricultural Sciences:58984", author = "Manpreet Singh and Parminder Kaur Wadhwa and Surinder Kaur", title = "Predicting Protein Function using Decision Tree", abstract = "The drug discovery process starts with protein
identification because proteins are responsible for many functions
required for maintenance of life. Protein identification further needs
determination of protein function. Proposed method develops a
classifier for human protein function prediction. The model uses
decision tree for classification process. The protein function is
predicted on the basis of matched sequence derived features per each
protein function. The research work includes the development of a
tool which determines sequence derived features by analyzing
different parameters. The other sequence derived features are
determined using various web based tools.", keywords = "Sequence Derived Features, decision tree.", volume = "2", number = "3", pages = "84-4", }