{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T06:32:11Z","timestamp":1743143531040,"version":"3.40.3"},"publisher-location":"Cham","reference-count":45,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030937355"},{"type":"electronic","value":"9783030937362"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-93736-2_2","type":"book-chapter","created":{"date-parts":[[2022,2,17]],"date-time":"2022-02-17T21:02:28Z","timestamp":1645131748000},"page":"15-25","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Interpretable Models via\u00a0Pairwise Permutations Algorithm"],"prefix":"10.1007","author":[{"given":"Troy","family":"Maasland","sequence":"first","affiliation":[]},{"given":"Jo\u00e3o","family":"Pereira","sequence":"additional","affiliation":[]},{"given":"Diogo","family":"Bastos","sequence":"additional","affiliation":[]},{"given":"Marcus","family":"de Goffau","sequence":"additional","affiliation":[]},{"given":"Max","family":"Nieuwdorp","sequence":"additional","affiliation":[]},{"given":"Aeilko H.","family":"Zwinderman","sequence":"additional","affiliation":[]},{"given":"Evgeni","family":"Levin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,17]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/s10260-006-0025-5","volume":"16","author":"H Kiers","year":"2007","unstructured":"Kiers, H., Smilde, A.: A comparison of various methods for multivariate regression with highly collinear variables. Stat. Meth. Appl. 16, 193 (2007)","journal-title":"Stat. Meth. Appl."},{"issue":"1","key":"2_CR2","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Mach. Learn. 45(1), 5\u201332 (2001)","journal-title":"Mach. Learn."},{"key":"2_CR3","unstructured":"Fisher, A., Rudin, C., Dominici, F.: Model class reliance: variable importance measures for any machine learning model class, from the \u201cRashomon\u201d perspective (2018)"},{"key":"2_CR4","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1186\/1471-2105-9-307","volume":"9","author":"C Strobl","year":"2008","unstructured":"Strobl, C., Boulesteix, A.L., Kneib, T., Augustin, T., Zeileis, A.: Conditional variable importance for random forests. BMC Bioinform. 9, 307 (2008). https:\/\/doi.org\/10.1186\/1471-2105-9-307","journal-title":"BMC Bioinform."},{"key":"2_CR5","unstructured":"Lundberg, S.M., Lee, S.-I.: A unified approach to interpreting model predictions. In: Advances in Neural Information Processing Systems (2017)"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Ribeiro, M., Singh, S., Guestrin, C.: Why should i trust you?: explaining the predictions of any classifier. eprint arXiv:1602.04938 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Pereira, J., Groen, A.K., Stroes, E.S.G., Levin, E.: Graph space embedding. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence Main Track, pp. 3253\u20133259 (2019). https:\/\/doi.org\/10.24963\/ijcai.2019\/451","DOI":"10.24963\/ijcai.2019\/451"},{"key":"2_CR8","unstructured":"Shrikumar, A., Greenside, P., Kundaje, A.: Learning important features through propagating activation differences. arXiv preprint arXiv:1704.02685 (2017)"},{"key":"2_CR9","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1016\/j.cmet.2017.09.008","volume":"26","author":"RS Kootte","year":"2017","unstructured":"Kootte, R.S., et al.: Improvement of insulin sensitivity after lean donor feces in metabolic syndrome is driven by baseline intestinal microbiota composition. Cell Metab. 26, 611\u2013619 (2017)","journal-title":"Cell Metab."},{"key":"2_CR10","first-page":"1833","volume":"11","author":"M Ojala","year":"2010","unstructured":"Ojala, M., Garriga, G.C.: Permutation tests for studying classifier performance. J. Mach. Learn. Res. 11, 1833\u20131863 (2010)","journal-title":"J. Mach. Learn. Res."},{"key":"2_CR11","unstructured":"Hooker, G., Mentch, L.: Please stop permuting features an explanation and alternatives. arXiv preprint arXiv:1905.03151v1 (2019)"},{"issue":"4","key":"2_CR12","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1198\/tast.2009.08199","volume":"63","author":"U Gr\u00f6mping","year":"2009","unstructured":"Gr\u00f6mping, U.: Variable importance assessment in regression: linear regression versus Random Forest. Am. Stat. 63(4), 308\u2013319 (2009). https:\/\/doi.org\/10.1198\/tast.2009.08199","journal-title":"Am. Stat."},{"issue":"14","key":"2_CR13","doi-asserted-by":"publisher","first-page":"1986","DOI":"10.1093\/bioinformatics\/btr300","volume":"27","author":"L Tolosi","year":"2011","unstructured":"Tolosi, L., Lengauer, T.: Classification with correlated features: unreliability of feature ranking and solutions. Bioinformatics 27(14), 1986\u20131994 (2011)","journal-title":"Bioinformatics"},{"issue":"3","key":"2_CR14","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1007\/s11222-016-9646-1","volume":"27","author":"B Gregorutti","year":"2016","unstructured":"Gregorutti, B., Michel, B., Saint-Pierre, P.: Correlation and variable importance in random forests. Stat. Comput. 27(3), 659\u2013678 (2016). https:\/\/doi.org\/10.1007\/s11222-016-9646-1","journal-title":"Stat. Comput."},{"key":"2_CR15","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1016\/j.ymeth.2015.03.017","volume":"83","author":"S Imangaliyev","year":"2015","unstructured":"Imangaliyev, S., Keijser, B., Crielaard, W., Tsivtsivadze, E.: Personalized microbial network inference via co-regularized spectral clustering. Methods 83, 28\u201335 (2015)","journal-title":"Methods"},{"key":"2_CR16","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1214\/07-EJS039","volume":"1","author":"H Ishwaran","year":"2007","unstructured":"Ishwaran, H., et al.: Variable importance in binary regression trees and forests. Electron. J. Stat. 1, 519\u2013537 (2007)","journal-title":"Electron. J. Stat."},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Caruana, R., Niculescu-Mizil, A., Crew, G., et al.: Ensemble selection from libraries of models. In: 21st International Conference on Machine Learning, ICML 2004, vol. 18. ACM (2004)","DOI":"10.1145\/1015330.1015432"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785\u2013794. ACM (2016)","DOI":"10.1145\/2939672.2939785"},{"key":"2_CR19","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1111\/j.1467-9868.2010.00740.x","volume":"72","author":"N Meinshausen","year":"2010","unstructured":"Meinshausen, N., B\u00fchlmann, P.: Stability selection. J. R. Stat. Soc. Ser. B (Stat. Methodol.) 72, 417\u2013473 (2010)","journal-title":"J. R. Stat. Soc. Ser. B (Stat. Methodol.)"},{"key":"2_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/gigascience\/giz042","volume":"8","author":"P Vangay","year":"2019","unstructured":"Vangay, P., Hillmann, B.M., Knights, D.: Microbiome Learning Repo (ML Repo): a public repository of microbiome regression and classification tasks. GigaScience 8, 1\u201312 (2019)","journal-title":"GigaScience"},{"key":"2_CR21","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1038\/nature11450","volume":"490","author":"J Qin","year":"2012","unstructured":"Qin, J., Li, Y., Cai, Z., et al.: A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490, 55\u201360 (2012)","journal-title":"Nature"},{"key":"2_CR22","unstructured":"Cohen, M.R.: The New Chinese Medicine Handbook: An Innovative Guide to Integrating Eastern Wisdom with Western Practice for Modern Healing, Fair Winds Press (2015)"},{"key":"2_CR23","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s10068-010-0015-0","volume":"19","author":"SY Chang","year":"2010","unstructured":"Chang, S.Y., Kim, D.-H., Han, M.J.: Physicochemical and sensory characteristics of soy yogurt fermented with Bifidobacterium breve K-110, Streptococcus thermophilus 3781, or Lactobacillus acidophilus Q509011. Food Sci. Biotechnol. 19, 107\u2013113 (2010). https:\/\/doi.org\/10.1007\/s10068-010-0015-0","journal-title":"Food Sci. Biotechnol."},{"issue":"2","key":"2_CR24","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1016\/j.fm.2013.01.012","volume":"34","author":"R Bedani","year":"2013","unstructured":"Bedani, R., Rossi, E.A., Isay Saad, S.M.: Impact of inulin and okara on Lactobacillus acidophilus La-5 and Bifidobacterium animalis Bb-12 viability in a fermented soy product and probiotic survival under in vitro simulated gastrointestinal conditions. Food Microbiol. 34(2), 382\u2013389 (2013)","journal-title":"Food Microbiol."},{"issue":"4","key":"2_CR25","first-page":"23","volume":"11","author":"H Kanda","year":"1976","unstructured":"Kanda, H., Wang, H.L., Hesseltine, C.W., et al.: Yoghurt production by Lactobacillus fermentation of soybean milk. Process Biochem. 11(4), 23 (1976)","journal-title":"Process Biochem."},{"issue":"1","key":"2_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.nutres.2009.11.004","volume":"30","author":"DY Kwon","year":"2010","unstructured":"Kwon, D.Y., Daily, J.W., III., Kim, H.J.: Antidiabetic effects of fermented soybean products on type 2 diabetes. Nutr. Res. 30(1), 1\u201313 (2010)","journal-title":"Nutr. Res."},{"key":"2_CR27","doi-asserted-by":"publisher","first-page":"1033","DOI":"10.1007\/s00394-011-0276-2","volume":"51","author":"NT Mueller","year":"2012","unstructured":"Mueller, N.T., Odegaard, A.O., Gross, M.D., et al.: Soy intake and risk of type 2 diabetes mellitus in Chinese Singaporeans. Eur. J. Nutr. 51, 1033\u20131040 (2012)","journal-title":"Eur. J. Nutr."},{"issue":"9","key":"2_CR28","doi-asserted-by":"publisher","first-page":"3321","DOI":"10.1021\/acs.jproteome.7b00319","volume":"16","author":"LH M\u00fcnger","year":"2017","unstructured":"M\u00fcnger, L.H., Trimigno, A., Picone, G., et al.: Identification of urinary food intake biomarkers for milk, cheese, and soy-based drink by untargeted GC-MS and NMR in healthy humans. J. Proetome Res. 16(9), 3321\u20133335 (2017)","journal-title":"J. Proetome Res."},{"issue":"7","key":"2_CR29","doi-asserted-by":"publisher","first-page":"2533","DOI":"10.1128\/aem.60.7.2533-2537.1994","volume":"60","author":"GM Cook","year":"1994","unstructured":"Cook, G.M., Wells, J.E., Russell, J.B.: Ability of Acidaminococcus Fermentans to oxidize trans-aconitate and decrease the accumulation of tricarballylate, a toxic end product of ruminal fermentation. Appl. Environ. Microbiol. 60(7), 2533\u20132537 (1994)","journal-title":"Appl. Environ. Microbiol."},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Moens, F., Verce, M., De Vuyst, L.: Lactate- and acetate-based cross-feeding interactions betweeen selected strains of Lactobacilli. Bifidobacteria and colon bacteria in the presence of inulin-type fructans. Int. J. Food Microbiol. 241, 225\u2013236 (2017)","DOI":"10.1016\/j.ijfoodmicro.2016.10.019"},{"issue":"2","key":"2_CR31","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.copbio.2009.01.002","volume":"20","author":"DMA Saulnier","year":"2009","unstructured":"Saulnier, D.M.A., Spinler, J.K., Gibson, G.R., et al.: Mechanisms of Probiosis and Prebiosis: considerations for enhanced functional foods. Curr. Opin. Biotechnol. 20(2), 135\u2013141 (2009)","journal-title":"Curr. Opin. Biotechnol."},{"issue":"4","key":"2_CR32","doi-asserted-by":"publisher","first-page":"1238","DOI":"10.2337\/db12-0526","volume":"62","author":"MC de Goffau","year":"2013","unstructured":"de Goffau, M.C., Luopaj\u00e4rvi, K., Knip, M., et al.: Fecal microbiota composition differs between children with beta-cell autoimmunity and those without. Diabetes 62(4), 1238\u20131244 (2013)","journal-title":"Diabetes"},{"issue":"3","key":"2_CR33","doi-asserted-by":"publisher","first-page":"198","DOI":"10.4093\/dmj.2015.39.3.198","volume":"39","author":"KY Hur","year":"2015","unstructured":"Hur, K.Y., Lee, M.-S.: Gut microbiota and metabolic disorders. Diabetes Metab. J. 39(3), 198\u2013203 (2015)","journal-title":"Diabetes Metab. J."},{"issue":"1","key":"2_CR34","doi-asserted-by":"publisher","first-page":"159","DOI":"10.2337\/dc14-0769","volume":"38","author":"AV Hartstra","year":"2015","unstructured":"Hartstra, A.V., Bouter, K.E.C., B\u00e4ckhed, F., et al.: Insights into the role of the microbiome in obesity and type 2 diabetes. Diabetes Care 38(1), 159\u2013165 (2015)","journal-title":"Diabetes Care"},{"key":"2_CR35","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1186\/1741-7015-11-46","volume":"11","author":"M Murri","year":"2013","unstructured":"Murri, M., Leiva, I., Gomez-Zumaquero, J.M., et al.: Gut microbiota in children with type 1 diabetes differs from that in healthy children: a case-control study. BMC Med. 11, 46 (2013)","journal-title":"BMC Med."},{"issue":"1","key":"2_CR36","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1530\/JME-19-0132","volume":"64","author":"MH Noureldein","year":"2020","unstructured":"Noureldein, M.H., Bitar, S., Youssef, N.: Butyrate modulates Diabetes-linked gut dysbiosis: epigenetic and mechanistic modifications. J. Mol. Endocrinol. 64(1), 29\u201342 (2020)","journal-title":"J. Mol. Endocrinol."},{"key":"2_CR37","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s40168-016-0163-4","volume":"4","author":"D Endesfelder","year":"2016","unstructured":"Endesfelder, D., Engel, M., Davis-Richardson, A.G., et al.: Towards a functional hypothesis relating anti-islet cell autoimmunity to the dietary impact on microbial communities and butyrate production. Microbiome 4, 17 (2016)","journal-title":"Microbiome"},{"issue":"1","key":"2_CR38","doi-asserted-by":"publisher","first-page":"7046","DOI":"10.1038\/s41598-017-07335-0","volume":"7","author":"L Jia","year":"2017","unstructured":"Jia, L., Li, D., Feng, N., et al.: Anti-diabetic effects of clostridium butyricum CGMCC0313.1 through promoting the growth of gut butyrate-producing bacteria in Type 2 Diabetic Mice. Sci. Rep. 7(1), 7046 (2017)","journal-title":"Sci. Rep."},{"key":"2_CR39","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.cbi.2016.06.007","volume":"254","author":"S Khan","year":"2016","unstructured":"Khan, S., Jena, G.: Sodium butyrate reduces insulin-resistance, fat accumulation and dyslipidemia in Type-2 Diabetic rat: a comparative study with metformin. Chem. Biol. Interact. 254, 124\u2013134 (2016)","journal-title":"Chem. Biol. Interact."},{"issue":"5","key":"2_CR40","doi-asserted-by":"publisher","first-page":"517","DOI":"10.1016\/j.cmet.2011.02.018","volume":"13","author":"DR Donohoe","year":"2012","unstructured":"Donohoe, D.R., Garge, N., Zhang, X., et al.: The microbiome and butyrate regulate energy metabolism and autophagy in the mammalian colon. Cell Metab. 13(5), 517\u2013526 (2012)","journal-title":"Cell Metab."},{"key":"2_CR41","doi-asserted-by":"crossref","unstructured":"Pereira, J., Groen, A.K., Stroes, E.S.G., Levin, E.: Graph space embedding. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 (2019)","DOI":"10.24963\/ijcai.2019\/451"},{"issue":"Pt A","key":"2_CR42","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1016\/j.phrs.2016.09.009","volume":"113","author":"S Sharma","year":"2016","unstructured":"Sharma, S., Taliyan, R.: Histone deacetylase inhibitors: future therapeutics for insulin resistance and type 2 diabetes. Pharmacol. Res. 113(Pt A), 320\u2013326 (2016)","journal-title":"Pharmacol. Res."},{"issue":"43","key":"2_CR43","doi-asserted-by":"publisher","first-page":"17598","DOI":"10.1074\/jbc.M117.804328","volume":"292","author":"E Dirice","year":"2017","unstructured":"Dirice, E., Ng, R.W.S., Martinez, R., et al.: Isoform-selective inhibitor of histone deacetylase 3 (HDAC3) limits pancreatic islet infiltration and protects female nonobese diabetic mice from diabetes. J. Biol. Chem. 292(43), 17598\u201317608 (2017)","journal-title":"J. Biol. Chem."},{"issue":"4","key":"2_CR44","doi-asserted-by":"publisher","first-page":"669","DOI":"10.2217\/epi.15.20","volume":"7","author":"S Khan","year":"2015","unstructured":"Khan, S., Jena, G.: The role of butyrate, a histone deacetylase inhibitor in diabetes mellitus: experimental evidence for therapeutic intervention. Epigenomics 7(4), 669\u2013680 (2015)","journal-title":"Epigenomics"},{"issue":"10","key":"2_CR45","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1038\/nrendo.2015.128","volume":"11","author":"EE Canfora","year":"2015","unstructured":"Canfora, E.E., Jocken, J.W., Blaak, E.E.: Short-chain fatty acids in control of body weight and insulin sensitivity. Nat. Rev. Endocrinol. 11(10), 577\u2013591 (2015)","journal-title":"Nat. Rev. Endocrinol."}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-93736-2_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T02:11:15Z","timestamp":1651803075000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93736-2_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030937355","9783030937362"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93736-2_2","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"17 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"869","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"210","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"24% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-4","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3-9","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"The conference was held online due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}