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:1.3872

@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