{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T14:02:29Z","timestamp":1724594549238},"reference-count":68,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2019,7,1]],"date-time":"2019-07-01T00:00:00Z","timestamp":1561939200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"name":"Mato Grosso State Government in Brazil and its Scientific Police (POLITEC-MT), Brazil"},{"DOI":"10.13039\/501100002322","name":"CAPES - Brazilian Coordination for the Improvement of Higher Education Personnel, Brazil","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002322","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001807","name":"FAPESP - S\u00e3o Paulo State Research Support Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003593","name":"CNPq - National Council for Scientific and Technological Development, Brazil","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Knowledge-Based Systems"],"published-print":{"date-parts":[[2019,7]]},"DOI":"10.1016\/j.knosys.2019.03.014","type":"journal-article","created":{"date-parts":[[2019,3,23]],"date-time":"2019-03-23T02:56:33Z","timestamp":1553309793000},"page":"36-49","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":18,"special_numbering":"C","title":["An analytical threshold for combining Bayesian Networks"],"prefix":"10.1016","volume":"175","author":[{"given":"Tadeu Junior","family":"Gross","sequence":"first","affiliation":[]},{"given":"Michel","family":"Bessani","sequence":"additional","affiliation":[]},{"given":"Willian","family":"Darwin Junior","sequence":"additional","affiliation":[]},{"given":"Renata Bezerra","family":"Ara\u00fajo","sequence":"additional","affiliation":[]},{"given":"Francisco Assis Carvalho","family":"Vale","sequence":"additional","affiliation":[]},{"given":"Carlos Dias","family":"Maciel","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.knosys.2019.03.014_b1","series-title":"Probabilistic Reasoning in Intelligent Systems","author":"Pearl","year":"1988"},{"key":"10.1016\/j.knosys.2019.03.014_b2","series-title":"Learning Bayesian Networks, Vol. 38","author":"Neapolitan","year":"2004"},{"key":"10.1016\/j.knosys.2019.03.014_b3","series-title":"Probabilistic Graphical Models: Principles and Techniques","author":"Koller","year":"2009"},{"key":"10.1016\/j.knosys.2019.03.014_b4","series-title":"Causality","author":"Pearl","year":"2009"},{"key":"10.1016\/j.knosys.2019.03.014_b5","series-title":"Causal Iinference in Statistics: A Primer","author":"Pearl","year":"2016"},{"issue":"9","key":"10.1016\/j.knosys.2019.03.014_b6","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1038\/nmeth.3550","article-title":"Points of significance: bayesian networks","volume":"12","author":"Puga","year":"2015","journal-title":"Nature Methods"},{"key":"10.1016\/j.knosys.2019.03.014_b7","first-page":"131","article-title":"Bayesian networks in neuroscience: a survey","volume":"8","author":"Bielza","year":"2014","journal-title":"Front. Computat. Neurosci."},{"key":"10.1016\/j.knosys.2019.03.014_b8","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.neucom.2014.08.089","article-title":"Integrated modular bayesian networks with selective inference for context-aware decision making","volume":"163","author":"Lee","year":"2015","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2019.03.014_b9","doi-asserted-by":"crossref","first-page":"77","DOI":"10.3389\/fnagi.2017.00077","article-title":"A bayesian model for the prediction and early diagnosis of alzheimer\u2019s disease","volume":"9","author":"Alexiou","year":"2017","journal-title":"Front. Aging Neurosci."},{"key":"10.1016\/j.knosys.2019.03.014_b10","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.neucom.2018.04.033","article-title":"Exploiting efficient and effective lazy semi-bayesian strategies for text classification","volume":"307","author":"Viegas","year":"2018","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2019.03.014_b11","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.artmed.2018.07.003","article-title":"Dependence between cognitive impairment and metabolic syndrome applied to a brazilian elderly dataset","volume":"90","author":"Gross","year":"2018","journal-title":"Artif. Intell. Med."},{"issue":"18","key":"10.1016\/j.knosys.2019.03.014_b12","doi-asserted-by":"crossref","first-page":"i632","DOI":"10.1093\/bioinformatics\/btq392","article-title":"Modeling associations between genetic markers using bayesian networks","volume":"26","author":"Villanueva","year":"2010","journal-title":"Bioinformatics"},{"key":"10.1016\/j.knosys.2019.03.014_b13","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.neucom.2017.01.092","article-title":"Learning bayesian network structures under incremental construction curricula","volume":"258","author":"Zhao","year":"2017","journal-title":"Neurocomputing"},{"issue":"1977","key":"10.1016\/j.knosys.2019.03.014_b14","first-page":"28","article-title":"Counting unlabeled acyclic digraphs","volume":"622","author":"Robinson","year":"1977","journal-title":"Comb. Math. V"},{"key":"10.1016\/j.knosys.2019.03.014_b15","unstructured":"D.M. Chickering, D. Geiger, D. Heckerman, et al. Learning Bayesian Networks is NP-hard, Tech. rep., MSR-TR-94-17, Microsoft Research, 1994."},{"issue":"4","key":"10.1016\/j.knosys.2019.03.014_b16","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/S1665-6423(13)71566-9","article-title":"Structure learning of bayesian networks by estimation of distribution algorithms with transpose mutation","volume":"11","author":"Kim","year":"2013","journal-title":"JART"},{"key":"10.1016\/j.knosys.2019.03.014_b17","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.neucom.2012.10.035","article-title":"Efficient methods for learning bayesian network super-structures","volume":"123","author":"Villanueva","year":"2014","journal-title":"Neurocomputing"},{"key":"10.1016\/j.knosys.2019.03.014_b18","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.procs.2016.08.092","article-title":"A novel structure learning algorithm for optimal bayesian network: best parents","volume":"96","author":"Kreimer","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"10.1016\/j.knosys.2019.03.014_b19","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.knosys.2016.07.031","article-title":"A parallel algorithm for bayesian network structure learning from large data sets","volume":"117","author":"Madsen","year":"2017","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2019.03.014_b20","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.knosys.2017.01.029","article-title":"A new hybrid method for learning bayesian networks: separation and reunion","volume":"121","author":"Liu","year":"2017","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2019.03.014_b21","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.ijar.2017.10.023","article-title":"On pruning with the mdl score","volume":"92","author":"Chen","year":"2018","journal-title":"Internat. J. Approx. Reason."},{"key":"10.1016\/j.knosys.2019.03.014_b22","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.122","article-title":"Finding the optimal bayesian network given a constraint graph","volume":"3","author":"Schreiber","year":"2017","journal-title":"PeerJ Comput. Sci."},{"issue":"164","key":"10.1016\/j.knosys.2019.03.014_b23","first-page":"1","article-title":"Pomegranate: fast and flexible probabilistic modeling in python","volume":"18","author":"Schreiber","year":"2018","journal-title":"J. Mach. Learn. Res."},{"issue":"3","key":"10.1016\/j.knosys.2019.03.014_b24","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1109\/TIT.1968.1054142","article-title":"Approximating discrete probability distributions with dependence trees","volume":"14","author":"Chow","year":"1968","journal-title":"IEEE Trans. Inform. Theory"},{"key":"10.1016\/j.knosys.2019.03.014_b25","series-title":"Proc. Workshop on Uncertainty in Artificial Intelligence","first-page":"222","article-title":"The recovery of causal polytrees from statistical data","author":"Rebane","year":"1987"},{"issue":"2","key":"10.1016\/j.knosys.2019.03.014_b26","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/0004-3702(90)90060-D","article-title":"The computational complexity of probabilistic inference using bayesian belief networks","volume":"42","author":"Cooper","year":"1990","journal-title":"Artificial Intelligence"},{"key":"10.1016\/j.knosys.2019.03.014_b27","series-title":"Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence","first-page":"196","article-title":"Data analysis with bayesian networks: a bootstrap approach","author":"Friedman","year":"1999"},{"key":"10.1016\/j.knosys.2019.03.014_b28","unstructured":"M. Scutari, C.E. Graafland, J.M. Guti\u00e9rrez, Who Learns Better Bayesian Network Structures: Constraint-Based, Score-based or Hybrid Algorithms?, arXiv preprint arXiv:1805.11908."},{"key":"10.1016\/j.knosys.2019.03.014_b29","series-title":"Proceedings of Sixth Conference on Uncertainty in Artificial Intelligence","first-page":"220","article-title":"Equivalence and synthesis of causal models","author":"Verma","year":"1991"},{"issue":"3","key":"10.1016\/j.knosys.2019.03.014_b30","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.artmed.2012.12.006","article-title":"Identifying significant edges in graphical models of molecular networks","volume":"57","author":"Scutari","year":"2013","journal-title":"Artif. Intell. Med."},{"issue":"17","key":"10.1016\/j.knosys.2019.03.014_b31","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1093\/bioinformatics\/btg313","article-title":"Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic bayesian networks","volume":"19","author":"Husmeier","year":"2003","journal-title":"Bioinformatics"},{"issue":"3","key":"10.1016\/j.knosys.2019.03.014_b32","first-page":"1","article-title":"Bagging statistical network inference from large-scale gene expression data","volume":"7","author":"de\u00a0Matos\u00a0Simoes","year":"2012","journal-title":"PLOS ONE"},{"issue":"4","key":"10.1016\/j.knosys.2019.03.014_b33","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/23249935.2016.1265019","article-title":"Causal structure learning for travel mode choice using structural restrictions and model averaging algorithm","volume":"13","author":"Ma","year":"2017","journal-title":"Transportmetrica A"},{"key":"10.1016\/j.knosys.2019.03.014_b34","series-title":"Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence","first-page":"87","article-title":"A transformational characterization of equivalent bayesian network structures","author":"Chickering","year":"1995"},{"issue":"13","key":"10.1016\/j.knosys.2019.03.014_b35","doi-asserted-by":"crossref","first-page":"S10","DOI":"10.1186\/1471-2105-13-S13-S10","article-title":"Model averaging strategies for structure learning in bayesian networks with limited data","volume":"13","author":"Broom","year":"2012","journal-title":"BMC Bioinformatics"},{"issue":"3","key":"10.1016\/j.knosys.2019.03.014_b36","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/BF00994016","article-title":"Learning bayesian networks: the combination of knowledge and statistical data","volume":"20","author":"Heckerman","year":"1995","journal-title":"Mach. Learn."},{"key":"10.1016\/j.knosys.2019.03.014_b37","series-title":"Bayesian Theory","author":"Bernardo","year":"2009"},{"issue":"8","key":"10.1016\/j.knosys.2019.03.014_b38","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1073\/pnas.1715306115","article-title":"Maximizing the information learned from finite data selects a simple model","volume":"115","author":"Mattingly","year":"2018","journal-title":"Proc. Natl. Acad. Sci."},{"issue":"8","key":"10.1016\/j.knosys.2019.03.014_b39","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.0030129","article-title":"A primer on learning in bayesian networks for computational biology","volume":"3","author":"Needham","year":"2007","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.knosys.2019.03.014_b40","series-title":"Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence","first-page":"360","article-title":"On sensitivity of the map bayesian network structure to the equivalent sample size parameter","author":"Silander","year":"2007"},{"key":"10.1016\/j.knosys.2019.03.014_b41","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.knosys.2018.03.007","article-title":"Novel binary encoding water cycle algorithm for solving bayesian network structures learning problem","volume":"150","author":"Wang","year":"2018","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.knosys.2019.03.014_b42","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.ijar.2018.08.002","article-title":"On scoring maximal ancestral graphs with the maxmin hill climbing algorithm","volume":"102","author":"Tsirlis","year":"2018","journal-title":"Internat. J. Approx. Reason."},{"issue":"4","key":"10.1016\/j.knosys.2019.03.014_b43","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1016\/j.ijar.2012.09.004","article-title":"Scaling up the greedy equivalence search algorithm by constraining the search space of equivalence classes","volume":"54","author":"Alonso-Barba","year":"2013","journal-title":"Internat. J. Approx. Reason."},{"key":"10.1016\/j.knosys.2019.03.014_b44","first-page":"507","article-title":"Optimal structure identification with greedy search","volume":"3","author":"Chickering","year":"2002","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"10.1016\/j.knosys.2019.03.014_b45","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1007\/s10618-010-0178-6","article-title":"Learning bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood","volume":"22","author":"G\u00e1mez","year":"2011","journal-title":"Data Mining Knowl. Discov."},{"key":"10.1016\/j.knosys.2019.03.014_b46","series-title":"An Introduction to the Bootstrap","author":"Efron","year":"1994"},{"key":"10.1016\/j.knosys.2019.03.014_b47","series-title":"Model Selection and Model Averaging","author":"Claeskens","year":"2008"},{"key":"10.1016\/j.knosys.2019.03.014_b48","first-page":"369","article-title":"Bootstrap analysis of gene networks based on bayesian networks and nonparametric regression","volume":"13","author":"Imoto","year":"2002","journal-title":"Genome Inform."},{"key":"10.1016\/j.knosys.2019.03.014_b49","series-title":"Bayesian Networks in R: With Applications in Systems Biology","author":"Nagarajan","year":"2013"},{"issue":"1","key":"10.1016\/j.knosys.2019.03.014_b50","first-page":"55","article-title":"On the number of cycles in a graph","volume":"21","author":"Harary","year":"1971","journal-title":"Matematicky casopis"},{"key":"10.1016\/j.knosys.2019.03.014_b51","series-title":"Advances in Neural Information Processing Systems","article-title":"DAGs with NO TEARS: continuous optimization for structure learning","author":"Zheng","year":"2018"},{"issue":"1","key":"10.1016\/j.knosys.2019.03.014_b52","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1137\/0204007","article-title":"Finding all the elementary circuits of a directed graph","volume":"4","author":"Johnson","year":"1975","journal-title":"SIAM J. Comput."},{"issue":"2","key":"10.1016\/j.knosys.2019.03.014_b53","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1111\/j.2517-6161.1988.tb01721.x","article-title":"Local computations with probabilities on graphical structures and their application to expert systems","volume":"50","author":"Lauritzen","year":"1988","journal-title":"J. R. Stat. Soc. Ser. B Stat. Methodol."},{"key":"10.1016\/j.knosys.2019.03.014_b54","series-title":"Bayesian Networks: with Examples in R","author":"Scutari","year":"2014"},{"issue":"5721","key":"10.1016\/j.knosys.2019.03.014_b55","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1126\/science.1105809","article-title":"Causal protein-signaling networks derived from multiparameter single-cell data","volume":"308","author":"Sachs","year":"2005","journal-title":"Science"},{"key":"10.1016\/j.knosys.2019.03.014_b56","series-title":"Advances in Artificial Intelligence","first-page":"366","article-title":"Random generation of bayesian networks","author":"Ide","year":"2002"},{"issue":"8","key":"10.1016\/j.knosys.2019.03.014_b57","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to roc analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"issue":"1","key":"10.1016\/j.knosys.2019.03.014_b58","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13040-017-0155-3","article-title":"Ten quick tips for machine learning in computational biology","volume":"10","author":"Chicco","year":"2017","journal-title":"BioData Mining"},{"issue":"2","key":"10.1016\/j.knosys.2019.03.014_b59","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/0005-2795(75)90109-9","article-title":"Comparison of the predicted and observed secondary structure of t4 phage lysozyme","volume":"405","author":"Matthews","year":"1975","journal-title":"Biochimica Biophys. Acta (BBA) - Protein Struct."},{"issue":"6","key":"10.1016\/j.knosys.2019.03.014_b60","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0177678","article-title":"Optimal classifier for imbalanced data using matthews correlation coefficient metric","volume":"12","author":"Boughorbel","year":"2017","journal-title":"PLOS ONE"},{"issue":"3","key":"10.1016\/j.knosys.2019.03.014_b61","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.jalz.2011.03.008","article-title":"The diagnosis of mild cognitive impairment due to alzheimers disease: recommendations","volume":"7","author":"Albert","year":"2011","journal-title":"Alzheimer\u2019s Dementia"},{"key":"10.1016\/j.knosys.2019.03.014_b62","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1109\/MCSE.2007.58","article-title":"Python for scientific computing","volume":"9","author":"Oliphant","year":"2007","journal-title":"Comput. Sci. Eng."},{"key":"10.1016\/j.knosys.2019.03.014_b63","series-title":"Proceedings of the 7th Python in Science Conference (SciPy2008)","first-page":"11","article-title":"Exploring network structure, dynamics, and function using networkx","author":"Hagberg","year":"2008"},{"key":"10.1016\/j.knosys.2019.03.014_b64","first-page":"159","article-title":"Pebl: inferring the structure of bayesian networks from knowledge and data","volume":"10","author":"Shah","year":"2009","journal-title":"JMLR"},{"key":"10.1016\/j.knosys.2019.03.014_b65","series-title":"Mastering Probabilistic Graphical Models Using Python","author":"Ankan","year":"2015"},{"issue":"2","key":"10.1016\/j.knosys.2019.03.014_b66","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1079\/BJN2001487","article-title":"Anthropometric measurements in the elderly: age and gender differences","volume":"87","author":"Perissinotto","year":"2002","journal-title":"Br. J. Nutrition"},{"issue":"1","key":"10.1016\/j.knosys.2019.03.014_b67","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1017\/S095925980600195X","article-title":"Under-nutrition in old age: diagnosis and management","volume":"16","author":"Watson","year":"2006","journal-title":"Rev. Clinical Gerontol."},{"key":"10.1016\/j.knosys.2019.03.014_b68","series-title":"Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)","author":"Cover","year":"2006"}],"container-title":["Knowledge-Based Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705119301352?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0950705119301352?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T02:25:10Z","timestamp":1721096710000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0950705119301352"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7]]},"references-count":68,"alternative-id":["S0950705119301352"],"URL":"https:\/\/doi.org\/10.1016\/j.knosys.2019.03.014","relation":{},"ISSN":["0950-7051"],"issn-type":[{"value":"0950-7051","type":"print"}],"subject":[],"published":{"date-parts":[[2019,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An analytical threshold for combining Bayesian Networks","name":"articletitle","label":"Article Title"},{"value":"Knowledge-Based Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.knosys.2019.03.014","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2019 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}