@article { ISI:000371228200032,
	title = {Prediction of reactivity ratios in free radical copolymerization from monomer resonance-polarity (Q-e) parameters: genetic programming-based models},
	journal = {International Journal of Chemical Reactor Engineering},
	volume = {14},
	number = {1},
	year = {2016},
	month = {FEB},
	pages = {361-372},
	publisher = {WALTER DE GRUYTER GMBH},
	address = {GENTHINER STRASSE 13, D-10785 BERLIN, GERMANY},
	abstract = {<p>The principal deficiency of the widely utilized Alfrey-Price (AP) scheme for computing reactivity ratios in the widely used free radical copolymerization is that it ignores important factors, such as the steric effects. This often leads to inaccurate reactivity ratio predictions by AP model. Accordingly, in this study, exclusively data-driven, Q-e parameter-based new models have been developed for the reactivity ratio prediction in free radical copolymerization. In the model development, a novel artificial intelligence formalism known as {\textquoteleft}{\textquoteleft}genetic programming (GP){\textquoteright}{\textquoteright} that performs symbolic regression has been employed. The GP-based models possess a different functional form than AP model. Further, parameters of GP-based models were fine-tuned using Levenberg-Marquardt (LM) nonlinear regression method. A comparison of AP, GP and GP-LM as well as artificial neural network (ANN)-based models indicates that GP and GP-LM models exhibit superior reactivity ratio prediction accuracy and generalization performance (with correlation coefficient magnitudes close to or greater than 0.9) when compared with AP and ANN models. The GPbased reactivity ratio prediction models developed here due to their higher accuracy and generalization capability have the potential of replacing the widely used AP models.</p>},
	keywords = {Alfrey-Price scheme, free radical copolymerization, genetic programming, reactivity ratio, symbolic regression},
	issn = {2194-5748},
	doi = {10.1515/ijcre-2014-0039},
	author = {Shrinivas, K. and Kulkarni, Rahul P. and Shaikh, Saif and Ghorpade, Ravindra V. and Vyas, Renu and Tambe, Sanjeev S. and Ponrathnam, S. and Kulkarni, Bhaskar 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.}
}
