@conference {664,
	title = {Hybrid mathematical modeling decodes the complexity of sphingolipid pathway to predict chemosensitivity},
	booktitle = {2017 International Conference and Workshop on Bioinspired Intelligence (IWOBI)},
	year = {2017},
	month = {July},
	keywords = {cancer, chemotherapy, Computer architecture, drugs, EDO, fluorescence, Fuzzy logic, GMM, Inhibitors, Mathematical model, Microprocessors, Sphingolipids},
	doi = {10.1109/IWOBI.2017.7985532},
	author = {Molina-Mora, J.A. and Kop-Montero, M. and Crespo-Marino, J.L. and Quiros, S. and Mora-Rodriguez, R.A.}
}
@conference {653,
	title = {Identification of cancer chemosensitivity by ODE and GMM modeling of heterogeneous cellular response to perturbations in fluorescent sphingolipid metabolism},
	booktitle = {2016 IEEE 36th Central American and Panama Convention (CONCAPAN XXXVI)},
	year = {2016},
	month = {Nov},
	keywords = {biochemistry, biomedical optical imaging, biosensors, BxPC3 cell lines, cancer, cancer chemosensitivity, cell classification, cellular biophysics, chemotherapy, Data models, drugs, dynamical mathematical model, EDO, flow cytometry, fluorescence, fluorescent sphingolipid metabolism, Gaussian processes, Gaussian-mixture model, gemcitabine response sensor, glucosyl-ceramide, GMM, GMM modeling, heterogeneous cellular response, image features, Inhibitors, lipid bilayers, Mathematical model, MiaPaca-4, mixture models, ODE modeling, optical sensors, pancreatic cancer, pathway dynamics, physiological models, single cell level, Sphingolipids, sphingomyelin-synthase, tumor, tumours},
	doi = {10.1109/CONCAPAN.2016.7942347},
	author = {J. Molina-Mora and R. Mora-Rodriguez}
}
