Ontology based personalized modeling for chronic disease risk evaluation and knowledge discovery: an integrated approach

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[1]  Sayan Mukherjee,et al.  Transductive Learning via Model Selection , 2004 .

[2]  Jiang Li,et al.  Transductive inference for color-based particle filter tracking , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[3]  M. Rewers,et al.  Global Estimates for Prevalence of Diabetes Mellitus and Impaired Glucose Tolerance in Adults , 1993, Diabetes Care.

[4]  Nikola K. Kasabov,et al.  NFI: a neuro-fuzzy inference method for transductive reasoning , 2005, IEEE Transactions on Fuzzy Systems.

[5]  N. Rigby,et al.  The obesity campaign view of , 2003 .

[6]  Mahadev Satyanarayanan,et al.  Pervasive computing: vision and challenges , 2001, IEEE Wirel. Commun..

[7]  B. C. Vickery,et al.  Ontologies , 1997, J. Inf. Sci..

[8]  Kevin Knight,et al.  Toward Distributed Use of Large-Scale Ontologies t , 1997 .

[9]  Rod Jackson,et al.  Web-based assessment of cardiovascular disease risk in routine primary care practice in New Zealand: the first 18,000 patients (PREDICT CVD-1). , 2006, The New Zealand medical journal.

[10]  A J Palmer,et al.  The Mt. Hood challenge: cross-testing two diabetes simulation models. , 2000, Diabetes research and clinical practice.

[11]  Farmer,et al.  Predicting chaotic time series. , 1987, Physical review letters.

[12]  M. Ashburner,et al.  The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration , 2007, Nature Biotechnology.

[13]  Bob J. Wielinga,et al.  Using explicit ontologies in KBS development , 1997, Int. J. Hum. Comput. Stud..

[14]  S. Heymsfield,et al.  Race-ethnicity-specific waist circumference cutoffs for identifying cardiovascular disease risk factors. , 2005, The American journal of clinical nutrition.

[15]  G. Bray,et al.  Obesity , 2008, Annals of Internal Medicine.

[16]  Jose Manuel Corera,et al.  Building and Reusing Ontologies for Electrical Network Applications , 1996, ECAI.

[17]  K. Anderson,et al.  Cardiovascular disease risk profiles. , 1991, American heart journal.

[18]  R. Jackson,et al.  Updated New Zealand cardiovascular disease risk-benefit prediction guide , 2000, BMJ : British Medical Journal.

[19]  Thorsten Joachims,et al.  Transductive Inference for Text Classification using Support Vector Machines , 1999, ICML.

[20]  Li-Xin Wang,et al.  Adaptive fuzzy systems and control - design and stability analysis , 1994 .

[21]  Asunción Gómez-Pérez,et al.  Building Ontologies at the Knowledge Level using the Ontology Design Environment , 1998 .

[22]  R. Whittaker,et al.  Will a web-based cardiovascular disease (CVD) risk assessment programme increase the assessment of CVD risk factors for Maori? , 2006, The New Zealand medical journal.

[23]  D. Cusi,et al.  α-Adducin and angiotensin-converting enzyme polymorphisms in hypertension: evidence for a joint influence on albuminuria , 2006, Journal of hypertension.

[24]  Nikola Kasabov,et al.  A Novel On-line , Evolving Clustering Method and Its Applications , 2001 .

[25]  David M Eddy,et al.  Archimedes: a trial-validated model of diabetes. , 2003, Diabetes care.

[26]  Martin L. King,et al.  Towards a Methodology for Building Ontologies , 1995 .

[27]  C. Meisinger,et al.  Sex differences in risk factors for incident type 2 diabetes mellitus: the MONICA Augsburg cohort study. , 2002, Archives of internal medicine.

[28]  Michael Y. Galperin.  The Molecular Biology Database Collection: 2005 update , 2004, Nucleic Acids Res..

[29]  C. S. George Lee.  Neuro‐Fuzzy Systems , 2007 .

[30]  Richard Fikes,et al.  The Ontolingua Server: a tool for collaborative ontology construction , 1997, Int. J. Hum. Comput. Stud..

[31]  D. Levy,et al.  Apolipoprotein E genotype and cardiovascular disease in the Framingham Heart Study. , 2001, Atherosclerosis.

[32]  D. Corella,et al.  Nutrition, Genomics, and Cardiovascular Disease Risk , 2007 .

[33]  Claude Lenfant,et al.  World Heart Day 2002: the international burden of cardiovascular disease: responding to the emerging global epidemic. , 2002, Circulation.

[34]  Bruce W. Porter,et al.  Knowledge Patterns , 2000, Handbook on Ontologies.

[35]  J. Mendel.  Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[36]  Rodney Jackson,et al.  Framingham Heart Study risk equation predicts first cardiovascular event rates in New Zealanders at the population level. , 2003, The New Zealand medical journal.

[37]  Jaakko Tuomilehto,et al.  The diabetes risk score: a practical tool to predict type 2 diabetes risk. , 2003, Diabetes care.

[38]  S. Wild,et al.  Global prevalence of diabetes: estimates for the year 2000 and projections for 2030. , 2004, Diabetes care.

[39]  G. Colditz,et al.  Weight Gain as a Risk Factor for Clinical Diabetes Mellitus in Women , 1995, Annals of Internal Medicine.

[40]  Rainer Fuchs,et al.  Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters , 2001, Bioinform..

[41]  Guoping Wang,et al.  Learning with progressive transductive support vector machine , 2003, Pattern Recognit. Lett..

[42]  A. Astrup,et al.  Redefining Type 2 diabetes: ‘Diabesity’ or ‘Obesity Dependent Diabetes Mellitus’? , 2000, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[43]  Doulaye Dembélé,et al.  Fuzzy C-means Method for Clustering Microarray Data , 2003, Bioinform..

[44]  Russel Pears,et al.  Integrating Local and Personalised Modelling with Global Ontology Knowledge Bases for Biomedical and Bioinformatics Decision Support , 2008, Computational Intelligence in Biomedicine and Bioinformatics.

[45]  Nikola K. Kasabov,et al.  TWNFI - a transductive neuro-fuzzy inference system with weighted data normalization for personalized modeling , 2006, Neural Networks.

[46]  Lotfi A. Zadeh,et al.  Fuzzy Logic , 2009, Encyclopedia of Complexity and Systems Science.

[47]  R. Rodriguez,et al.  Nutritional genomics: the next frontier in the postgenomic era , 2003 .

[48]  P. Stover.  Nutritional genomics. , 2004, Physiological genomics.

[49]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[50]  R. Nesto.  Comprehensive clinical assessment of modifiable cardiometabolic risk factors , 2008 .

[51]  M Aickin,et al.  The global diabetes model: user friendly version 3.0. , 2000, Diabetes research and clinical practice.

[52]  J. Mckinlay,et al.  US public health and the 21st century: diabetes mellitus , 2000, The Lancet.

[53]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[54]  Chris F. Taylor,et al.  The MGED Ontology: a resource for semantics-based description of microarray experiments , 2006, Bioinform..

[55]  Z. Bosnic,et al.  Evaluation of prediction reliability in regression using the transduction principle , 2003, The IEEE Region 8 EUROCON 2003. Computer as a Tool..

[56]  Asunción Gómez-Pérez,et al.  Methodologies, tools and languages for building ontologies: Where is their meeting point? , 2003, Data Knowl. Eng..

[57]  K. Chien.  An Accurate Risk Score Based on Anthropometric, Dietary, and Lifestyle Factors to Predict the Development of Type 2 Diabetes , 2007, Diabetes Care.

[58]  Domenico M. Pisanelli,et al.  Ontologies in Medicine , 2004 .

[59]  James A. Hendler,et al.  The Semantic Web" in Scientific American , 2001 .

[60]  B. Wilson,et al.  Obesity and body fat distribution in the New Zealand population. , 2001, The New Zealand medical journal.

[61]  Ramanathan V. Guha,et al.  Building Large Knowledge-Based Systems: Representation and Inference in the Cyc Project , 1990 .

[62]  Deborah L. McGuinness,et al.  The Chimaera Ontology Environment , 2000, AAAI/IAAI.

[63]  K. Boini,et al.  Obesity: An overview on its current perspectives and treatment options , 2004, Nutrition journal.

[64]  Anson,et al.  DIET , LIFESTYLE , AND THE RISK OF TYPE 2 DIABETES MELLITUS IN WOMEN , 2001 .

[65]  A. Sheikh,et al.  Predicting cardiovascular risk in England and Wales: prospective derivation and validation of QRISK2 , 2008, BMJ : British Medical Journal.

[66]  J. Neel.  Diabetes mellitus: a "thrifty" genotype rendered detrimental by "progress"? , 1962, American journal of human genetics.

[67]  Nikola K. Kasabov,et al.  Adaptive modeling and discovery in bioinformatics: The evolving connectionist approach , 2008, Int. J. Intell. Syst..

[68]  York Sure,et al.  Ontoedit : Collaborative ontology engineering for the semantic web , 2002 .

[69]  Matjaz Kukar,et al.  Transductive reliability estimation for medical diagnosis , 2003, Artif. Intell. Medicine.

[70]  Timothy W. Finin,et al.  Enabling Technology for Knowledge Sharing , 1991, AI Mag..

[71]  P. Kopelman.  Obesity as a medical problem , 2000, Nature.

[72]  Nikola Kasabov,et al.  Evolving ontologies for intelligent decision support , 2006, Fuzzy Logic and the Semantic Web.

[73]  Jim Kaput,et al.  Diet-disease gene interactions. , 2004, Nutrition.

[74]  Neil Chapman,et al.  Managing the global burden of cardiovascular disease , 2002 .

[75]  G A Colditz,et al.  Weight as a risk factor for clinical diabetes in women. , 1990, American journal of epidemiology.

[76]  Patrick Lambrix,et al.  Evaluation of ontology development tools for bioinformatics , 2003, Bioinform..

[77]  Stephen Cranefield,et al.  UML for ontology development , 2002, The Knowledge Engineering Review.

[78]  J. Diamond,et al.  The double puzzle of diabetes , 2003, Nature.

[79]  Nikola Kasabov,et al.  Fuzzy clustering of gene expression data , 2002, 2002 IEEE World Congress on Computational Intelligence. 2002 IEEE International Conference on Fuzzy Systems. FUZZ-IEEE'02. Proceedings (Cat. No.02CH37291).

[80]  Michael Gruninger,et al.  Methodology for the Design and Evaluation of Ontologies , 1995, IJCAI 1995.

[81]  Claude Bouchard,et al.  Genes, Fat Intake, and Cardiovascular Disease Risk Factors in the Quebec Family Study , 2007, Obesity.

[82]  G. Burch.  [Cardiovascular diseases]. , 1956, Revista medica cubana.

[83]  A. Leong.  Diet, Nutrition, and the Prevention of Chronic Diseases , 1992 .

[84]  Michael Y. Galperin.  The Molecular Biology Database Collection: 2007 update , 2006, Nucleic Acids Res..

[85]  Michael Uschold,et al.  Ontologies: principles, methods and applications , 1996, The Knowledge Engineering Review.

[86]  Therapies Study Investigators.  The need for a large-scale trial of fibrate therapy in diabetes: the rationale and design of the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) study. [ISRCTN64783481] , 2004 .

[87]  Bernhard Schölkopf,et al.  Feature selection and transduction for prediction of molecular bioactivity for drug design , 2003, Bioinform..

[88]  Henrik Eriksson,et al.  The evolution of Protégé: an environment for knowledge-based systems development , 2003, Int. J. Hum. Comput. Stud..

[89]  Balakrishnan Chandrasekaran,et al.  What are ontologies, and why do we need them? , 1999, IEEE Intell. Syst..

[90]  G. Colditz,et al.  The disease burden associated with overweight and obesity. , 1999, JAMA.

[91]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[92]  Tianxi Cai,et al.  Joint Effects of Common Genetic Variants on the Risk for Type 2 Diabetes in U.S. Men and Women of European Ancestry , 2009, Annals of Internal Medicine.

[93]  Carmel Domshlak,et al.  Rank Aggregation for Automatic Schema Matching , 2007, IEEE Transactions on Knowledge and Data Engineering.

[94]  J. Nicholson.  Global systems biology, personalized medicine and molecular epidemiology , 2006, Molecular systems biology.

[95]  Thomas R. Gruber,et al.  A translation approach to portable ontology specifications , 1993 .

[96]  Jens C. Brüning,et al.  Inactivation of the Fto gene protects from obesity , 2009, Nature.

[97]  R. Scott Crowder,et al.  Predicting the Mackey-Glass Timeseries With Cascade-Correlation Learning , 1990 .

[98]  L. Penland,et al.  Use of a cDNA microarray to analyse gene expression patterns in human cancer , 1996, Nature Genetics.

[99]  Michael Y. Galperin.  The Molecular Biology Database Collection: 2006 update , 2005, Nucleic Acids Res..

[100]  Alexander C. Yu,et al.  Methods in biomedical ontology , 2006, J. Biomed. Informatics.

[101]  Steffen Staab,et al.  Ontology Learning for the Semantic Web , 2002, IEEE Intell. Syst..

[102]  Dieter Fensel,et al.  Knowledge Engineering: Principles and Methods , 1998, Data Knowl. Eng..

[103]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[104]  S. Yusuf,et al.  Global burden of cardiovascular diseases: part I: general considerations, the epidemiologic transition, risk factors, and impact of urbanization. , 2001, Circulation.

[105]  R. Spang,et al.  Predicting the clinical status of human breast cancer by using gene expression profiles , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[106]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[107]  Ken Williams,et al.  Validation of Prediction of Diabetes by the Archimedes Model and Comparison With Other Predicting Models , 2008, Diabetes Care.

[108]  A. Dowell,et al.  Risk factors for type 2 diabetes in postmenopausal New Zealand women: a cross-sectional study. , 2004, The New Zealand medical journal.

[109]  Alisdair Owens,et al.  Semantic Storage: Overview and Assessment , 2005 .

[110]  Nikola Kasabov,et al.  Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines , 2002, IEEE Transactions on Neural Networks.

[111]  Nikola Kasabov,et al.  Evolving Connectionist Systems: The Knowledge Engineering Approach , 2007 .

[112]  J. Després,et al.  International Day for the Evaluation of Abdominal Obesity (IDEA): A Study of Waist Circumference, Cardiovascular Disease, and Diabetes Mellitus in 168 000 Primary Care Patients in 63 Countries , 2007, Circulation.

[113]  Xiangyi Lu,et al.  Drosophila nutrigenomics can provide clues to human gene-nutrient interactions. , 2005, Annual review of nutrition.

[114]  Nicola Guarino.  UNDERSTANDING, BUILDING, AND USING ONTOLOGIES , 1997 .

[115]  Alexander Gammerman,et al.  Transductive Confidence Machines for Pattern Recognition , 2002, ECML.

[116]  A. T. Schreiber,et al.  Reusable and shareable Knowledge Bases: A European Perspective , 1994 .

[117]  H. King,et al.  Global Burden of Diabetes, 1995–2025: Prevalence, numerical estimates, and projections , 1998, Diabetes Care.

[118]  V. R. Benjamins,et al.  WonderTools? A comparative study of ontological engineering tools , 2000, Int. J. Hum. Comput. Stud..

[119]  J. G. Murray,et al.  Blood Pressure , 1911, The Indian medical gazette.

[120]  Carole A. Goble,et al.  An ontology for bioinformatics applications , 1999, Bioinform..

[121]  J. Neel.  The genetics of diabetes mellitus. , 1970, Advances in metabolic disorders.

[122]  N. Christakis,et al.  The Spread of Obesity in a Large Social Network Over 32 Years , 2007, The New England journal of medicine.

[123]  David M Eddy,et al.  Validation of the archimedes diabetes model. , 2003, Diabetes care.

[124]  Valentina Tamma.  An example of food ontology for diabetes control , 2005 .

[125]  M. Eriksen,et al.  Regional fat localizations and racial/ethnic variations in odds of hypertension in at-risk American adults , 2006, Journal of Human Hypertension.

[126]  H. Lunt,et al.  Diabetes in New Zealand. , 2000, Diabetes research and clinical practice.

[127]  Sebastián M. Real,et al.  Polymorphism of the FABP2 gene: a population frequency analysis and an association study with cardiovascular risk markers in Argentina , 2007, BMC Medical Genetics.

[128]  A. Levey,et al.  A More Accurate Method To Estimate Glomerular Filtration Rate from Serum Creatinine: A New Prediction Equation , 1999, Annals of Internal Medicine.

[129]  P Piot,et al.  The global epidemic. , 1998, AIDS care.

[130]  W. Jim Zheng,et al.  Integration of the Gene Ontology into an object-oriented architecture , 2005, BMC Bioinformatics.

[131]  T. Gill,et al.  Obesity: epidemiology and possible prevention. , 2002, Best practice & research. Clinical endocrinology & metabolism.

[132]  P. Zimmet.  Kelly West Lecture 1991 Challenges in Diabetes Epidemiology—From West to the Rest , 1992, Diabetes Care.

[133]  A Tremblay,et al.  Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. , 1990, Arteriosclerosis.

[134]  J F Sallis,et al.  Behavioral science research in diabetes: lifestyle changes related to obesity, eating behavior, and physical activity. , 2001, Diabetes care.

[135]  Shun-ichi Amari,et al.  Mathematical foundations of neurocomputing , 1990, Proc. IEEE.

[136]  John Domingue,et al.  Tadzebao and WebOnto: discussing, browsing, and editing ontologies on the Web , 1998 .

[137]  Tom Michael Mitchell,et al.  Explanation-based generalization: A unifying view , 1986 .

[138]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[139]  Nikola K. Kasabov,et al.  DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction , 2002, IEEE Trans. Fuzzy Syst..

[140]  Ayhan Demiriz,et al.  Semi-Supervised Support Vector Machines , 1998, NIPS.

[141]  Steffen Staab,et al.  Knowledge Processes and Ontologies , 2001, IEEE Intell. Syst..

[142]  D. Simmons.  Diabetes and its complications in New Zealand: an epidemiological perspective. , 1996, The New Zealand medical journal.

[143]  Dieter Fensel,et al.  Ontologies: A silver bullet for knowledge management and electronic commerce , 2002 .

[144]  FOOD COMES FIRST: M ETHODOLOGIES FOR THE NATIONAL NUTRITION SURVEY OF NEW ZEALAND FOOD COMES FIRST: M ETHODOLOGIES FOR THE NATIONAL NUTRITION SURVEY OF NEW ZEALAND PUBLIC HEALTH REPORT NUMBER 2 , 1997 .

[145]  Ian Horrocks,et al.  OilEd: a Reason-able Ontology Editor for the Semantic Web , 2001, Description Logics.

[146]  Thorsten Joachims,et al.  Transductive Learning via Spectral Graph Partitioning , 2003, ICML.

[147]  Chris F. Taylor,et al.  Development of FuGO: an ontology for functional genomics investigations. , 2006, Omics : a journal of integrative biology.

[148]  Michael Pignone,et al.  Framingham-based tools to calculate the global risk of coronary heart disease , 2003, Journal of general internal medicine.

[149]  Nello Cristianini,et al.  Large Margin Trees for Induction and Transduction , 1999, ICML.

[150]  J. Shaw,et al.  Metabolic syndrome—a new world‐wide definition. A Consensus Statement from the International Diabetes Federation , 2006, Diabetic medicine : a journal of the British Diabetic Association.

[151]  N. F. Noy,et al.  Ontology Development 101: A Guide to Creating Your First Ontology , 2001 .

[152]  J Tuomilehto,et al.  Diabetes risk score in Oman: a tool to identify prevalent type 2 diabetes among Arabs of the Middle East. , 2007, Diabetes research and clinical practice.

[153]  Asunción Gómez-Pérez,et al.  Towards a Method to Conceptualize Domain Ontologies , 1996 .

[154]  W. Hoy,et al.  Waist circumference, body mass index, hip circumference and waist-to-hip ratio as predictors of cardiovascular disease in Aboriginal people , 2004, European Journal of Clinical Nutrition.

[155]  Zakaria Maamar,et al.  What can context do for web services? , 2006, CACM.

[156]  Philip M. Long,et al.  Breast cancer classification and prognosis based on gene expression profiles from a population-based study , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[157]  Nikola K. Kasabov,et al.  Ontology Based Personalized Modeling for Chronic Disease Risk Analysis: An Integrated Approach , 2008, Aust. J. Intell. Inf. Process. Syst..

[158]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[159]  Nikola K. Kasabov,et al.  Global, local and personalised modeling and pattern discovery in bioinformatics: An integrated approach , 2007, Pattern Recognit. Lett..

[160]  Francesco Carlo Morabito,et al.  Ontology Based Personalized Modeling for Type 2 Diabetes Risk Analysis: An Integrated Approach , 2009, ICONIP.

[161]  W. N. Borst,et al.  Construction of Engineering Ontologies for Knowledge Sharing and Reuse , 1997 .

[162]  Thomas D. Schmittgen,et al.  Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. , 2001, Methods.

[163]  Douglas B. Lenat,et al.  CYC: a large-scale investment in knowledge infrastructure , 1995, CACM.

[164]  Harry Wechsler,et al.  Watch List Face Surveillance Using Transductive Inference , 2004, ICBA.

[165]  Shaoning Pang,et al.  Transductive support vector machines and applications in bioinformatics for promoter recognition , 2003, International Conference on Neural Networks and Signal Processing, 2003. Proceedings of the 2003.

[166]  G. Joshy,et al.  Epidemiology of diabetes in New Zealand: revisit to a changing landscape. , 2006, The New Zealand medical journal.