Application of Genetic Programming (GP) Formalism for Building Disease Predictive Models from Protein-Protein Interactions (PPI) Data

Created by W.Langdon from gp-bibliography.bib Revision:1.8010

@Article{Vyas:2016:TCBB,

• author = "Renu Vyas and Sanket Bapat and Purva Goel and M. Karthikeyan and S. S. Tambe and B. D. Kulkarni",
• journal = "IEEE/ACM Transactions on Computational Biology and Bioinformatics",
• title = "Application of Genetic Programming (GP) Formalism for Building Disease Predictive Models from Protein-Protein Interactions (PPI) Data",
• year = "2016",
• abstract = "Protein-protein interactions (PPIs) play a vital role in the biological processes involved in the cell functions and disease pathways. The experimental methods known to predict PPIs require tremendous efforts and the results are often hindered by the presence of a large number of false positives. Herein, we demonstrate the use of a new Genetic Programming (GP) based Symbolic Regression (SR) approach for predicting PPIs related to a disease. In a case study, a dataset consisting of one hundred and thirty five PPI complexes related to cancer was used to construct a generic PPI predicting model with good PPI prediction accuracy and generalisation ability. A high correlation coefficient(CC) of 0.893, low root mean square error (RMSE) and mean absolute percentage error (MAPE) values of 478.221 and 0.239, respectively were achieved for both the training and test set outputs. To validate the discriminatory nature of the model, it was applied on a dataset of diabetes complexes where it yielded significantly low CC values. Thus, the GP model developed here serves a dual purpose: (a)a predictor of the binding energy of cancer related PPI complexes, and (b)a classifier for discriminating PPI complexes related to cancer from those of other diseases.",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/TCBB.2016.2621042",
• ISSN = "1545-5963",
• notes = "Also known as \cite{7707365}",

}

Genetic Programming entries for Renu Vyas Sanket Bapat Purva Goel M Karthikeyan Sanjeev S Tambe Bhaskar D Kulkarni

Citations