@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 {661,
	title = {Biocomputing platform module for cancer genomics and chemotherapy},
	booktitle = {2016 IEEE 36th Central American and Panama Convention (CONCAPAN XXXVI)},
	year = {2016},
	month = {Nov},
	keywords = {Analytical models, batched analysis, biocomputing, biocomputing platform module, Bioinformatics, cancer, cancer cell lines, cancer genomics, chemotherapeutic compounds, chemotherapeutic data, chemotherapy, data analysis, Data models, Data Processing, Data visualization, exploratory data analysis, gene expression, gene profile, genetics, Genomics, Learning Systems, medical computing, patient treatment, pattern clustering, Pattern Recognition, regression, regression analysis, unsupervised clustering models},
	doi = {10.1109/CONCAPAN.2016.7942342},
	author = {J. C. Coto and F. Siles and R. Mora-Rodriguez}
}
@conference {655,
	title = {Characterization of heterogeneous response to chemotherapy by perturbation-based modeling of fluorescent sphingolipid metabolism in cancer cell subpopulations},
	booktitle = {2016 IEEE 36th Central American and Panama Convention (CONCAPAN XXXVI)},
	year = {2016},
	month = {Nov},
	keywords = {biochemistry, biological organs, biomedical optical imaging, biosensors, cancer, cancer cell subpopulations, cancer heterogeneity, cancer treatments, cell classification, cell response, cellular biophysics, chemosensitive cell line, chemosensitivity, chemotherapy, drug, drugs, feature extraction, flow cytometry, fluorescence, fluorescent sphingolipid metabolism, fluorescent sphingolipid probe, Gaussian mixture models, Gaussian processes, GMM, heterogeneous response, image classification, image features, Immune system, Inhibitors, intratumoral heterogeneity, lipid bilayers, medical image processing, mixture models, optical sensors, pancreatic cancer, perturbation-based modeling, physiological models, single cell level, Sphingolipids, Statistics, systems biology, therapy resistance},
	doi = {10.1109/CONCAPAN.2016.7942346},
	author = {R. Mora-Rodriguez and J. Molina-Mora}
}
@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}
}
