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Journal Article
R. Vyas, Bapat, S., Goel, P., Karthikeyan, M., Tambe, S. S., and Kulkarni, B. D., Application of genetic programming (GP) formalism for building disease predictive models from protein-protein interactions (PPI) data, IEEE-ACM Transactions on Computational Biology and Bioinformatics, vol. 15, no. 1, pp. 27-37, 2018.
S. Tiwary, Ghugare, S. B., Chavan, P. D., Saha, S., Datta, S., Sahu, G., and Tambe, S. S., Co-gasification of high ash coal–biomass blends in a fluidized bed gasifier: experimental study and computational intelligence-based modeling, Waste and Biomass Valorization, vol. 11, no. 1, pp. 1-19, 2018.
P. Goel, Bapat, S., Vyas, R., Tambe, A., and Tambe, S. S., Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices, Journal of Chromatography A, vol. 1420, pp. 98-109, 2015.
V. Patil-Shinde, Saha, S., Sharma, B. K., Tambe, S. S., and Kulkarni, B. D., High ash char gasification in thermo-gravimetric analyzer and prediction of gasification performance parameters using computational intelligence formalisms, Chemical Engineering Communications, vol. 203, no. 8, pp. 1029-1044, 2016.
R. Vyas, Goel, P., Karthikeyan, M., Tambe, S. S., and Kulkarni, B. D., Pharmacokinetic modeling of caco-2 cell permeability using genetic programming (GP) method, Letters in Drug Design & Discovery, vol. 11, no. 9, pp. 1112-1118, 2014.
S. B. Ghugare, Tiwary, S., Elangovan, V., and Tambe, S. S., Prediction of higher heating value of solid biomass fuels using artificial intelligence formalisms, Bioenergy Research, vol. 7, no. 2, pp. 681-692, 2014.
K. Shrinivas, Kulkarni, R. P., Shaikh, S., Ghorpade, R. V., Vyas, R., Tambe, S. S., Ponrathnam, S., and Kulkarni, B. D., Prediction of reactivity ratios in free radical copolymerization from monomer resonance-polarity (Q-e) parameters: genetic programming-based models, International Journal of Chemical Reactor Engineering, vol. 14, no. 1, pp. 361-372, 2016.