Prediction of P53 Mutants (Multiple Sites) Transcriptional Activity Based on Structural (2D&3D) Properties

@inproceedings{Ramani2013PredictionOP,
  title={Prediction of P53 Mutants (Multiple Sites) Transcriptional Activity Based on Structural (2D&3D) Properties},
  author={R. Geetha Ramani and Shomona Gracia Jacob},
  booktitle={PloS one},
  year={2013}
}
Prediction of secondary site mutations that reinstate mutated p53 to normalcy has been the focus of intense research in the recent past owing to the fact that p53 mutants have been implicated in more than half of all human cancers and restoration of p53 causes tumor regression. However laboratory investigations are more often laborious and resource intensive but computational techniques could well surmount these drawbacks. In view of this, we formulated a novel approach utilizing computational… CONTINUE READING
Highly Cited
This paper has 116 citations. REVIEW CITATIONS

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.
Showing 1-10 of 12 extracted citations

Data mining technique for identification of diagnostic biomarker to predict Schizophrenia disorder

2014 IEEE International Conference on Computational Intelligence and Computing Research • 2014
View 1 Excerpt

Mining semantic representation from medical text: A Bayesian approach

2014 International Conference on Recent Trends in Information Technology • 2014

117 Citations

050100'14'16'18
Citations per Year
Semantic Scholar estimates that this publication has 117 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 71 references

Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy

IEEE Transactions on Pattern Analysis and Machine Intelligence • 2005
View 4 Excerpts
Highly Influenced

Global Suppressors of P53 Mutations

KR Brachmann
Publication Number: US2005/0065332 AI, PCT Filed: Jan 15, • 2005
View 4 Excerpts
Highly Influenced

Efficient Classifier for Classification of Hepatitis C Virus Clinical Data through Data Mining Algorithms and Techniques

SG Jacob, R Geetha Ramani, P Nancy
Proceedings of the International Conference on Computer Applications, • 2012
View 1 Excerpt

Evolving Efficient Classification Rules from Cardiotocography Data through Data Mining Methods and Techniques

SG Jacob, R Geetha Ramani
European Journal of Scientific Research, • 2012
View 1 Excerpt

Similar Papers

Loading similar papers…