Explorative Analytics & Networks – References

  1. Buscema P.M., A novel adapting mapping method for emergent properties discovery in data bases: experience in medical field, in “2007 IEEE International Conference on Systems, Man and Cybernetics (SMC 2007)”. Montreal, Canada, 7-10 Ottobre 2007.
  2. Buscema P.M., Grossi E., The Semantic Connectivity Map: an adapting self-organizing knowledge discovery method in data bases. Experience in Gastro-oesophageal reflux disease, Int. J. Data Mining and Bioinformatics, Vol. 2, No. 4, 2008.
  3. Buscema P.M., Grossi E., Snowdon D., Antuono P., Auto-Contractive Maps: an Artificial Adaptive System for Data Mining. An Application to Alzheimer Disease, in Current Alzheimer Research, 2008, 5, 481-498.
  4. Buscema P.M.(ed), Squashing Theory and Contractive Map Network, Semeion Technical Paper #32, Rome, 2007.
  5. Buscema P.M., Helgason C., Grossi E., Auto Contractive Maps, H Function and Maximally Regular Graph: Theory and Applications , Special Session on “Artificial Adaptive Systems in Medicine : applications in the real world, NAFIPS 2008 (IEEE), New York, May 19-22, 2008.
  6. Licastro F., Porcellini E., Chiappelli M., Forti P., Buscema P.M. et al., Multivariable network associated with cognitive decline and dementia, int. Neurobiology of Aging, Vol. 1, Issue 2, February 2010, 257-269.
  7. Buscema P.M. & Grossi E.(eds), Artificial Adaptive Systems in Medicine, Bentham e-books, 2009, 25-47.
  8. Buscema P.M. and Sacco P.L., Auto-contractive Maps, the H Function, and the Maximally Regular Graph (MRG): A New Methodology for Data Mining, in Capecchi V. et al. (eds.), Applications of Mathematics in Models, Artificial Neural Networks and Arts, Chapter 11, DOI 10.1007/978-90-481-8581-8_11, Springer Science+Business Media B.V. 2010.
  9. Grossi E., Tavano Blessi G., Sacco P.L., Buscema P.M., The Interaction Between Culture, Health and Psychological Well-Being: Data Mining from the Italian Culture and Well-Being Project, J Happiness Studies, Springer, 2011.
  10. Licastro F., Porcellini E., Forti P., Buscema P.M., Carbone I., Ravaglia G., Grossi E., Multi factorial interactions in the pathogenesis pathway of Alzheimer’s disease: a new risk charts for prevention of dementia, Immunity & Ageing 2010, 7(Suppl 1):S4.
  11. Buscema P.M., Newman F., Grossi E., Tastle W., Application of Adaptive Systems Methodology to Radiotherapy,  in NAFIPS 2010, 12-14 July, Toronto, Canada.
  12. Eller-Vainicher C., Zhukouskaya V.V., Tolkachev V., Koritko S.S., Cairoli E., Grossi E., Beck-Peccoz P., Chiodini I., Shepelkevich A.P., Low BoneMineral Density and Its Predictors in Type 1 Diabetic Patients Evaluated by the Classic Statistics and Artificial Neural Network Analysis, Diabetes Care , pp 1-6, 2011.
  13. Gomiero T., Croce L., Grossi E., De Vreese L., Buscema P.M., Mantesso U., De Bastiani E., A Short Version of SIS (Support Intensity Scale): The Utility of the Application of Artificial Adaptive Systems, US-China Education Review A 2 (2011) 196-207.
  14. Buscema P.M., Penco  and Grossi E., A Novel Mathematical Approach to Define the Genes/SNPs Conferring Risk or Protection in Sporadic Amyotrophic Lateral Sclerosis Based on Auto Contractive Map Neural Networks and Graph Theory, Neurology Research International, Volume 2012, Article ID 478560, 13 pages, doi:10.1155/2012/478560
  15. Grossi E., Compare A., Buscema P.M., The concept of individual semantic maps in clinical psychology: a feasibility study on a new paradigm, Quality & Quantity International Journal of Methodology , August 04th 2012, ISSN 0033-5177, Qual Quant, DOI 10.1007/s11135-012-9746-8
  16. Coppedè F., Grossi E., Buscema P.M., Migliore L., Application of Artificial Neural Networks to Investigate One-Carbon Metabolism in Alzheimer’s Disease and Healthy Matched Individuals, PLOS ONE, www.plosone.org, August 2013, Volume 8 , Issue 8 , e74012, pp 1-11.
  17. Street M.E., Buscema P.M.,  Smerieri A.,  Montanini L.,  Grossi E.,  Artificial Neural Networks, and Evolutionary Algorithms as a systems biology approach to a data-base on fetal growth restriction, in Prog Biophys Mol Biol., 2013, July, pages 1-6.
  18. Compare A., Grossi E., Buscema P.M., Zarbo C., Mao X., Faletra F., Pasotti E., Moccetti T., Mommersteeg P.M.C., and Auricchio A., Combining Personality Traits with Traditional Risk Factors for Coronary Stenosis: An Artificial Neural Networks Solution in Patients with Computed Tomography Detected Coronary Artery Disease, Cardiovascular Psychiatry and Neurology, Volume 2013, Article ID 814967, 9 pages, Hindawi Publishing Corporation. http://dx.doi.org/10.1155/2013/814967.
  19. Buscema P.M., Consonni V., Ballabio D., Mauri A., Massini G., Breda M., Todeschini R., K-CM: A new artificial neural network. Application to supervised pattern recognition,  Chemometrics and Intelligent Laboratory Systems 138 (2014) 110–119.
  20. Buscema P.M., Massini G. and Maurelli G., Artificial Neural Networks: An Overview and their Use, in the Analysis of the AMPHORA-3 Dataset, Substance Use & Misuse, Early Online:1–14, 2014.
  21. Gironi M., Borgiani B., Farina E., Mariani E., Cursano C., Alberoni M., Nemni R., Comi G., Buscema P.M., Furlan R, and Grossi E., A Global Immune Deficit in Alzheimer’s Disease and Mild Cognitive Impairment Disclosed by a Novel Data Mining Process, Journal of Alzheimer’s Disease 43 (2015) 1199–1213.
  22. Drenos F., Grossi E., Buscema P.M., Humphries S.E., Networks in Coronary Heart Disease Genetics As a Step towards Systems Epidemiology, PLoS ONE 10(5): May 7 (2015). e0125876. doi:10.1371/journal.pone.0125876.
  23. Coppedè F., Grossi E., Lopomo A., Spisni R., Buscema P.M., Migliore L., Application of artificial neural networks to link genetic and environmental factors to DNA methylation in colorectal cancer, Epigenomics (2015) 7(2), 175–186.
  24. Narzisi A., Muratori F., Buscema P.M., Calderoni S., Grossi E, Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks, Neuropsychiatric Disease and Treatment 2015:11 1587–1599.
  25. Buscema P.M., Grossi E., Montanini L., Street M.E., Data Mining of Determinants of Intrauterine Growth Retardation Revisited Using Novel Algorithms Generating Semantic Maps and Prototypical Discriminating Variable Profiles, PLoS ONE 10(7): June 9 (2015) e0126020. doi:10.1371/journal.
  26. Buscema P.M., Gitto L., Russo S., Marcellusi A., Fiori F., Maurelli G., MassiniG., Mennini F.S., The perception of corruption in health: AutoCM methods for an international comparison, Qual Quant DOI 10.1007/s11135-016-0315-4, Springer 2016.
  27. Ferilli G., Sacco P.L., Teti E., Buscema P.M., Top corporate brands and the global structure of country brand positioning: An AutoCM ANN approach, Expert Systems With Applications 66 (2016) 62–75.
  28. Buscema P.M., Sacco P.M., MST Fitness Index and implicit data narratives: A comparative test on alternative unsupervised algorithms, Physica A 461 (2016) 726–746.
  29. Buscema P.M., Ferilli G., Sacco P.L., What kind of “world order”? An artificial neural networks approach to intensive data mining, Technological Forecasting & Social Change 117 (2017) 46–56
  30. Mennini F.S., Gitto L., Russo S., Cicchetti A., Ruggeri M., Coretti S., Maurelli G., Buscema P.M., Does regional belonging explain the similarities in the expenditure determinants of Italian healthcare deliveries? An approach based on Artificial Neural Networks, Economic Analysis and Policy 55 (2017) 47–56.
  31. Buscema P.M. , Ferilli G. , Sacco P.L. , The meta-geography of the open society: An Auto-CM ANN approach, Expert Systems With Applications 99 (2018) 12–24.