@article { ISI:000341643300008,
	title = {Pharmacokinetic modeling of caco-2 cell permeability using genetic programming (GP) method},
	journal = {Letters in Drug Design \& Discovery},
	volume = {11},
	number = {9},
	year = {2014},
	month = {NOV},
	pages = {1112-1118},
	publisher = {BENTHAM SCIENCE PUBL LTD},
	address = {EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES},
	abstract = {<p>An accurate prediction of the pharmacokinetic properties of orally administered drugs is of paramount importance in pharmaceutical industry. Caco-2 cell permeability is a well established parameter for assessing the drug absorption profiles of lead molecules. Due to the restrictions on animal testing, prohibitive in situ models and ethical issues, the development of predictive models is essential. Genetic programming (GP) is an artificial intelligence (AI)-based exclusively data driven modeling paradigm. Given an example input-output data, it searches and optimizes, both the structure and parameters of a well fitting linear/non-linear input-output model. Despite this novelty, GP has not been widely exploited in drug design. Accordingly, in this study we propose a GP based approach for the in silico prediction of Caco-2 cell permeability using a diverse set of molecules. The predictions yielded a high magnitude for the training and test set correlation coefficient with low RMSE, indicating accurate Caco-2 permeability prediction and generalization performance by the GP model. The predictions were better or comparable to artificial neural networks (ANN) and support vector regression (SVR) methods. The GP based modeling approach illustrated will find diverse applications in (QSAR, QSPR and QSTR) modeling for the virtual screening of large libraries.</p>},
	keywords = {ADME modeling, Caco-2 cell permeability, genetic programming, MLP, SVR},
	issn = {1570-1808},
	author = {Vyas, Renu and Goel, Purva and Karthikeyan, Muthukumarasamy and Tambe, S. S. and Kulkarni, B. D.}
}
@article {44229,
	title = {Application of genetic programming (GP) formalism for building disease predictive models from protein-protein interactions (PPI) data},
	journal = {IEEE-ACM Transactions on Computational Biology and Bioinformatics},
	volume = {15},
	year = {2018},
	month = {FEB},
	pages = {27-37},
	type = {Article},
	abstract = {<p>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 this case study, a dataset consisting of 135 PPI complexes related to cancer was used to construct a generic PPI predicting model with good PPI prediction accuracy and generalization ability. A high correlation coefficient (CC) magnitude of 0.893, and 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.</p>},
	keywords = {Binding energy, cancer, Disease, genetic programming, machine learning, protein-protein interactions, symbolic regression},
	issn = {1545-5963},
	doi = {10.1109/TCBB.2016.2621042},
	author = {Vyas, Renu and Bapat, Sanket and Goel, Purva and Karthikeyan, Muthukumarasamy and Tambe, Sanjeev S. and Kulkarni, Bhaskar D.}
}
