Human Protein Function Prediction using Decision Tree Induction

@inproceedings{Singh2007HumanPF,
  title={Human Protein Function Prediction using Decision Tree Induction},
  author={Manpreet Singh and Parminder Kaur Wadhwa and Parvinder Singh Sandhu},
  year={2007}
}
To overcome the problem of exponentially increasing protein data, drug discoverers need efficient machine learning techniques to predict the functions of proteins which are responsible for various diseases in human body. The existing decision tree induction methodology C4.5 uses the entropy calculation for best attribute selection. The proposed method develops a new decision tree induction technique in which uncertainty measure is used for best attribute selection. This is based on the study of… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 21 references

Basics of Information Theory

  • D. S. Touretzky
  • 2004
2 Excerpts

Development and Applications of Decision Trees

  • H. Almuallim
  • Information and Computer Science Department,
  • 2003

Prediction of human protein function according to Gene Ontology Categories

  • R. Jensen, H. Gupta
  • in 2003 proceedings of Bioinformatics,
  • 2003

Prediction of human protein function according to Gene Ontology Categories ” in

  • H. Gupta R. Jensen
  • Defense of C 4 . 5 : Notes on Learning one…
  • 2003

Apweiler “ Automatic rule generation for protein annotation with the C 4 . 5 data mining algorithm applied on SWISSPROT ”

  • W. Fleischmann E. Kretschmann, R.
  • Fundamental Concepts of Bioinformatics
  • 2002

Prediction of Human Protein Function from Post-Translational Modifications and Localization Features

  • L. Jensen
  • Journal of Molecular Biology,
  • 2002

Prediction of Protein Function from Sequence Derived Protein Features

  • L. Jensen
  • Ph.D. thesis
  • 2002
1 Excerpt

Dehaspe “ Accurate prediction of protein functional class in the M . tuberculosis and E . coli genomes using data mining

  • A. Karwath, A. Clare, L.
  • Bioinformatics
  • 2001

Similar Papers

Loading similar papers…