N. Kasabov, Global, local and personalised modeling and pattern discovery in bioinformatics: An integrated approach, Pattern Recognition Letters, vol.28, issue.6, pp.673-685, 2007.
DOI : 10.1016/j.patrec.2006.08.007

T. R. Gruber, A translation approach to portable ontology specifications, Knowledge Acquisition, vol.5, issue.2, pp.199-220, 1993.
DOI : 10.1006/knac.1993.1008

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

D. Fensel, Ontologies: A Silver Bullet for Knowledge Management and Electronic Commerce, 2004.

B. Chandrasekaran, J. R. Josephson, and V. R. Benjamins, What are ontologies, and why do we need them? Intelligent Systems and Their Applications, pp.20-26, 1999.
DOI : 10.1109/5254.747902

A. Owens, Semantic Storage: Overview and Assessment, 2005.

T. Berners-lee, J. Hendler, and O. Lassila, The Semantic Web, Scientific American, vol.284, issue.5, 2001.
DOI : 10.1038/scientificamerican0501-34

J. B. Brown and A. J. Palmer, The Mt. Hood challenge: cross-testing two diabetes simulation models, Diabetes Research and Clinical Practice, vol.50, issue.3, pp.57-64, 2000.
DOI : 10.1016/S0168-8227(00)00217-5

URL : https://hal.archives-ouvertes.fr/hal-00856094

J. B. Brown and A. Russell, The global diabetes model: user friendly version 3.0, Diabetes Research and Clinical Practice, vol.50, issue.3, pp.15-46, 2000.
DOI : 10.1016/S0168-8227(00)00215-1

J. Lindstrom and J. Tuomilehto, The Diabetes Risk Score: A practical tool to predict type 2 diabetes risk, Diabetes Care, vol.26, issue.3, pp.725-731, 2003.
DOI : 10.2337/diacare.26.3.725

D. M. Eddy and L. Schlessinger, Archimedes: A trial-validated model of diabetes, Diabetes Care, vol.26, issue.11, pp.3093-3101, 2003.
DOI : 10.2337/diacare.26.11.3093

D. M. Eddy and L. Schlessinger, Validation of the Archimedes Diabetes Model, Diabetes Care, vol.26, issue.11, pp.3102-3110, 2003.
DOI : 10.2337/diacare.26.11.3102

J. A. Al-lawati and J. Tuomilehto, Diabetes risk score in Oman: A tool to identify prevalent type 2 diabetes among Arabs of the Middle East, Diabetes Research and Clinical Practice, vol.77, issue.3, pp.438-444, 2007.
DOI : 10.1016/j.diabres.2007.01.013

M. Cornelis and L. Qi, Joint Effects of Common Genetic Variants on the Risk for Type 2 Diabetes in U.S. Men and Women of European Ancestry, Annals of Internal Medicine, vol.150, issue.8, pp.541-550, 2009.
DOI : 10.7326/0003-4819-150-8-200904210-00008

M. Stern and K. Williams, Validation of Prediction of Diabetes by the Archimedes Model and Comparison With Other Predicting Models, Diabetes Care, vol.31, issue.8, pp.1670-1671, 2008.
DOI : 10.2337/dc08-0521

Q. Song and N. Kasabov, TWNFI ??? a transductive neuro-fuzzy inference system with weighted data normalization for personalized modeling, Neural Networks, vol.19, issue.10, pp.1591-1596, 2006.
DOI : 10.1016/j.neunet.2006.05.028

V. N. Vapnik, Statistical Learning Theory, 1998.

M. T. Mitchell and R. Keller, Explanation-based generalization: A unified view, Machine Learning, pp.47-80, 1997.

L. A. Zadeh, Fuzzy sets, Information and Control, vol.8, issue.3, pp.338-353, 1965.
DOI : 10.1016/S0019-9958(65)90241-X

L. A. Zadeh, Fuzzy logic, Computer, vol.21, issue.4, pp.83-93, 1988.
DOI : 10.1109/2.53

T. Takagi and M. Sugeno, Fuzzy identification of systems and its applications to modeling and control, IEEE Transactions on Systems, Man, and Cybernetics, vol.15, pp.116-132, 1985.

J. Fischer and L. Koch, Inactivation of the Fto gene protects from obesity, Nature, vol.134, issue.7240, pp.894-899, 2009.
DOI : 10.2337/diab.45.11.1455

A. Verma, An Integrated Approach for Ontology Based Personalized Modeling: Chronic Disease Ontology, Risk Evaluation and Knowledge Discovery, 2010.
DOI : 10.1007/978-3-642-02490-0_146

N. Kasabov, Q. Song, L. Benuskova, P. Gottgtroy, V. Jain et al., Integrating Local and Personalised Modelling with Global Ontology Knowledge Bases for Biomedical and Bioinformatics Decision Support, Computational Intelligence in Bioinformatics, 2008.
DOI : 10.1007/978-3-540-70778-3_4

N. Kasabov and Y. Hu, Integrated optimisation method for personalised modelling and case studies for medical decision support, International Journal of Functional Informatics and Personalised Medicine, vol.3, issue.3, pp.236-256, 2010.
DOI : 10.1504/IJFIPM.2010.039123

M. Fiasché, A. Verma, M. Cuzzola, P. Iacopino, N. Kasabov et al., Discovering Diagnostic Gene Targets and Early Diagnosis of Acute GVHD Using Methods of Computational Intelligence over Gene Expression Data, Artificial Neural Networks ? ICANN 2009. Part II, pp.10-19, 2009.
DOI : 10.1016/j.patrec.2006.08.007

M. Fiasché, M. Cuzzola, R. Fedele, P. Iacopino, and F. C. Morabito, Machine Learning and Personalized Modeling Based Gene Selection for Acute GvHD Gene Expression Data Analysis, Artificial Neural Networks ? proceedings of ICANN 2010 Part I, 2010.
DOI : 10.1007/978-3-642-15819-3_29

M. Fiasché, M. Cuzzola, G. Irrera, P. Iacopino, and F. C. Morabito, Advances in Medical Decision Support Systems for Diagnosis of Acute Graft-versus-Host Disease: Molecular and Computational Intelligence Joint Approaches, Frontiers in Biology, pp.10-1007

M. Fiasché, M. Cuzzola, P. Iacopino, N. Kasabov, and F. C. Morabito, Personalized Modeling based Gene Selection for acute GvHD Gene Expression Data Analysis: a Computational Framework Proposed, Machine Learning Applications, 2010.