Abstract
This chapter focuses on Auto-Contractive Maps, which is a particularly useful ANN. Moreover, the relationship between Auto-Contractive Map (Auto-CM), which is the main topic of this monograph, its relationship to other ANNs and some illustrative example applications are presented.
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Reference
Buscema, M., C. Helgason, and E. Grossi. 2008. Auto contractive maps, H function and maximally regular graph: Theory and applications. In Special session on “Artificial Adaptive Systems in Medicine: Applications in the Real World, NAFIPS 2008 (IEEE)”, New York, May 19–22, 2008.
General References
Buscema, M. (ed.). 2007. Squashing Theory and Contractive Map Network. Semeion Technical Paper #32, Rome.
Buscema, M. 2007. 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, Octobre 7–10, 2007.
Buscema, M., and E. Grossi. 2008. The Semantic Connectivity Map: An Adapting Self-organizing Knowledge Discovery Method in Data Bases. Experience in Gastro-oesophageal Reflux Disease. International Journal of Data Mining and Bioinformatics 2 (4).
Buscema, M., E. Grossi, D. Snowdon, and P. Antuono. 2008. Auto-contractive Maps: An Artificial Adaptive System for Data Mining. An Application to Alzheimer Disease. Current Alzheimer Research 5: 481–498.
Licastro, F., E. Porcellini, M. Chiappelli, P. Forti, M. Buscema, et al. 2010. Multivariable Network Associated with Cognitive Decline and Dementia. International Neurobiology of Aging 1 (2): 257–269.
Buscema, M., and E. Grossi (eds.). 2009. Artificial Adaptive Systems in Medicine, 25–47. Bentham e-books.
Buscema, Massimo, and Pier L. Sacco. 2010. Auto-contractive Maps, the H Function, and the Maximally Regular Graph (MRG): A New Methodology for Data Mining. In Applications of Mathematics in Models, Artificial Neural Networks and Arts, ed. V. Capecchi, et al. Berlin: Springer.
Grossi, Enzo, Giorgio Tavano Blessi, Pier Luigi Sacco, and Massimo Buscema. 2011. The Interaction Between Culture, Health and Psychological Well-Being: Data Mining from the Italian Culture and Well-Being Project. Journal of Happiness Studies (Springer).
Licastro, Federico, Elisa Porcellini, Paola Forti, Massimo Buscema, Ilaria Carbone, Giovanni Ravaglia, and Enzo Grossi. 2010. Multi Factorial Interactions in the Pathogenesis Pathway of Alzheimer’s Disease: A New Risk Charts for Prevention of Dementia. Immunity & Ageing 7 (Suppl 1): S4.
Buscema, M., F. Newman, E. Grossi, and W. Tastle. 2010. Application of Adaptive Systems Methodology to Radiotherapy. In NAFIP, Toronto, Canada, July 12–14, 2010.
Eller-Vainicher, C., V.V. Zhukouskaya, Y.V. Tolkachev, S.S. Koritko, E. Cairoli, E. Grossi, P. Beck-Peccoz, I. Chiodini, and A.P. Shepelkevich. 2011. Low Bone Mineral Density and Its Predictors in Type 1 Diabetic Patients Evaluated by the Classic Statistics and Artificial Neural Network Analysis. Diabetes Care 1–6.
Gomiero, T., L. Croce, E. Grossi, L. De Vreese, M. Buscema, U. Mantesso, and E. De Bastiani. 2011. A Short Version of SIS (Support Intensity Scale): The Utility of the Application of Artificial Adaptive Systems. US-China Education Review A 2: 196–207.
Buscema, M., S. Penco, and E. Grossi. 2012. 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 2012: 13, Article ID 478560.
Grossi, E., A. Compare, and M. Buscema. 2012. 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.
Coppedè, F., E. Grossi, M. Buscema, and L. Migliore. 2013. Application of Artificial Neural Networks to Investigate One-Carbon Metabolism in Alzheimer’s Disease and Healthy Matched Individuals. PLOS ONE 8 (8): e74012, 1–11.
Street, M.E., M. Buscema, A. Smerieri, L. Montanini, and E. Grossi. 2013. Artificial Neural Networks, and Evolutionary Algorithms as a Systems Biology Approach to a Data-base on Fetal Growth Restriction. Progress in Biophysics and Molecular Biology, 1–6.
Compare, A., E. Grossi, M. Buscema, C. Zarbo, X. Mao, F. Faletra, E. Pasotti, T. Moccetti, P.M.C. Mommersteeg, and A. Auricchio. 2013. 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 2013: 9, Article ID 814967 (Hindawi Publishing Corporation).
Buscema, M., V. Consonni, D. Ballabio, A. Mauri, G. Massini, M. Breda, and R. Todeschini. 2014. K-CM: A New Artificial Neural Network. Application to Supervised Pattern Recognition. Chemometrics and Intelligent Laboratory Systems 138: 110–119.
Buscema, M., G. Massini, and G. Maurelli. 2014. Artificial Neural Networks: An Overview and Their Use in the Analysis of the AMPHORA-3 Dataset. Substance Use & Misuse, Early Online: 1–14.
Gironi, M., B. Borgiani, E. Farina, E. Mariani, C. Cursano, M. Alberoni, R. Nemni, G. Comi, M. Buscema, R. Furlan, and Enzo Grossi. 2015. 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: 1199–1213.
Drenos, F., E. Grossi, M. Buscema, and S.E. Humphries. 2015. Networks in Coronary Heart Disease Genetics as a Step Towards Systems Epidemiology. PLoS ONE 10 (5): e0125876. https://doi.org/10.1371/journal.pone.0125876.
Coppedè, F., E. Grossi, A. Lopomo, R. Spisni, M. Buscema, and Lucia Migliore. 2015. Application of Artificial Neural Networks to Link Genetic and Environmental Factors to DNA Methylation in Colorectal Cancer. Epigenomics 7 (2): 175–186.
Narzisi, A., F. Muratori, M. Buscema, S. Calderoni, and E. Grossi. 2015. Outcome Predictors in Autism Spectrum Disorders Preschoolers Undergoing Treatment as Usual: Insights from an Observational Study Using Artificial Neural Networks. Neuropsychiatric Disease and Treatment 11: 1587–1599.
Buscema, M., E. Grossi, L. Montanini, M.E. Street. 2015. Data Mining of Determinants of Intrauterine Growth Retardation Revisited Using Novel Algorithms Generating Semantic Maps and Prototypical Discriminating Variable Profiles. PLoS ONE 10 (7): e0126020. https://doi.org/10.1371/journal.
Buscema, Paolo Massimo, Lara Gitto, Simone Russo, Andrea Marcellusi, Federico Fiori, Guido Maurelli, Giulia Massini, and Francesco Saverio Mennini. 2016. The Perception of Corruption in Health: AutoCM Methods for an International Comparison. Quality & Quantity. https://doi.org/10.1007/s11135-016-0315-4 (Springer).
Buscema, Massimo, Masoud Asadi-Zeydabadi, Weldon Lodwick, and Marco Breda. 2016. The H0 Function, a New Index for Detecting Structural/Topological Complexity Information in Undirected Graphs. Physica A 447: 355–378.
Buscema, Paolo Massimo, Guido Maurelli, Francesco Saverio Mennini, et al. 2016. Artificial Neural Networks and Their Potentialities in Analyzing Budget Health Data: An Application for Italy of What-If Theory. Quality & Quantity. https://doi.org/10.1007/s11135-016-0329-y (Springer).
Buscema, Massimo, and Pier Luigi Sacco. 2016. MST Fitness Index and Implicit Data Narratives: A Comparative Test on Alternative Unsupervised Algorithms. Physica A 461: 726–746.
Ferilli, Guido, Pier Luigi Sacco, Emanuele Teti, and Massimo Buscema. 2016. Top Corporate Brands and the Global Structure of Country Brand Positioning: An AutoCM ANN Approach. Expert Systems With Applications 66: 62–75.
Caffarra, Paolo, Simona Gardini, Francesca Dieci, et al. 2013. The Qualitative Scoring MMSE Pentagon Test (QSPT): A New Method for Differentiating Dementia with Lewy Body from Alzheimer’s Disease. Behavioural Neurology 27: 213–220. https://doi.org/10.3233/ben-120319.
Campisi, Giuseppina, Martina Chiappelli, Massimo De Martinis, Vito Franco, Lia Ginaldi, Rosario Guiglia, Federico Licastro, and Domenico Lio. 2009. Pathophysiology of Age-Related Diseases. Immunity & Ageing 6: 12. https://doi.org/10.1186/1742-4933-6-12.
Coppedè, Fabio, Enzo Grossi, Francesca Migheli, and Lucia Migliore. 2010. Polymorphisms in Folate-Metabolizing Genes, Chromosome Damage, and Risk of Down Syndrome in Italian Women: Identification of Key Factors Using Artificial Neural Networks. BMC Medical Genomics 3: 42.
De Benedetti, Stefano, Giorgio Lucchini, Alessandro Marocchi, Silvana Penco, Christian Lunetta, Stefania Iametti*, Elisabetta Gianazza, and Francesco Bonomi. 2015. Serum Metal Evaluation in a Small Cohort of Amyotrophic Lateral Sclerosis Patients Reveals High Levels of Thiophylic Species. Peptidomics 2: 29–34.
di Ludovico, Alessandro. 2008. Experimental approaches to glyptic art using artificial neural networks. An investigation into the Ur III iconological context. In Proceedings of the 36th CAA Conference, Budapest, April 2–6, 2008.
Di Ludovico, A., and G. Pieri. 2011. Artificial Neural Networks and Ancient Artefacts: Justifications for a Multiform Integrated Approach Using PST and Auto-CM Models. Archeologia e Calcolatori 22: 99–128.
Gallucci, Maurizio, Pierpaolo Spagnolo, and Maria Aricò. 2016. Predictors of Response to Cholinesterase Inhibitors Treatment of Alzheimer’s Disease: Date Mining from the TREDEM Registry. Journal of Alzheimer’s Disease 50: 969–979. https://doi.org/10.3233/JAD-150747.
Gironi, M., et al. 2013. A Novel Data Mining System Points Out Hidden Relationships Between Immunological Markers in Multiple Sclerosis. Immunity & Ageing 10: 1.
Grossi, E., S. Cazzaniga, S. Crotti, et al. 2014. The Constellation of Dietary Factors in Adolescent Acne: A Semantic Connectivity Map Approach. Journal of the European Academy of Dermatology and Venereology, December 2014. https://doi.org/10.1111/jdv.12878.
Knibbe, Ronald A., Mieke Derickx, Allaman Allamani, and Giulia Massini. 2014. Alcohol Consumption and Its Related Harms in the Netherlands Since 1960: Relationships with Planned and Unplanned Factors. Substance Use & Misuse, Early Online: 1–12.
Smerieri, Arianna, Chiara Testa, Pietro Lazzeroni, et al. 2015. Di-(2-Ethylhexyl) Phthalate Metabolites in Urine Show Age-Related Changes and Associations with Adiposity and Parameters of Insulin Sensitivity in Childhood. PLoS ONE 10 (2): e0117831. https://doi.org/10.1371/journal.pone.0117831.
Street, Maria E., Enzo Grossi, Cecilia Volta, Elena Faleschini, and Sergio Bernasconi. 2008. Placental Determinants of Fetal Growth: Identification of Key Factors in the Insulin-Like Growth Factor and Cytokine Systems Using Artificial Neural Networks. BMC Pediatrics 8: 24. https://doi.org/10.1186/1471-2431-8-24.
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Buscema, P.M., Massini, G., Breda, M., Lodwick, W.A., Newman, F., Asadi-Zeydabadi, M. (2018). Auto-contractive Maps. In: Artificial Adaptive Systems Using Auto Contractive Maps. Studies in Systems, Decision and Control, vol 131. Springer, Cham. https://doi.org/10.1007/978-3-319-75049-1_3
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