{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T05:32:59Z","timestamp":1763616779367,"version":"3.45.0"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032006516"},{"type":"electronic","value":"9783032006523"}],"license":[{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T00:00:00Z","timestamp":1755648000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-00652-3_18","type":"book-chapter","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T12:59:27Z","timestamp":1755608367000},"page":"245-262","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Translating Genes into Insight: Causal Genomics for\u00a0Diabetes Risk Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7372-1000","authenticated-orcid":false,"given":"Sheresh","family":"Zahoor","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0540-5053","authenticated-orcid":false,"given":"Pietro","family":"Li\u00f3","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5840-1603","authenticated-orcid":false,"given":"Ga\u00ebl","family":"Dias","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1838-0091","authenticated-orcid":false,"given":"Mohammed","family":"Hasanuzzaman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,8,20]]},"reference":[{"issue":"6","key":"18_CR1","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1016\/j.medj.2021.04.006","volume":"2","author":"L Adlung","year":"2021","unstructured":"Adlung, L., Cohen, Y., Mor, U., Elinav, E.: Machine learning in clinical decision making. Med 2(6), 642\u2013665 (2021)","journal-title":"Med"},{"key":"18_CR2","doi-asserted-by":"publisher","unstructured":"Ambags, E.L., Capitoli, G., Imperio, V.L., Provenzano, M., Nobile, M.S., Li\u00f2, P.: Assisting clinical practice with fuzzy probabilistic decision trees (2023). https:\/\/doi.org\/10.48550\/arXiv.2304.07788","DOI":"10.48550\/arXiv.2304.07788"},{"key":"18_CR3","doi-asserted-by":"crossref","unstructured":"Biecek, P., Burzykowski, T.: Explanatory Model Analysis: Explore, Explain, and Examine Predictive Models. Chapman and Hall\/CRC (2021)","DOI":"10.1201\/9780429027192"},{"key":"18_CR4","doi-asserted-by":"publisher","unstructured":"Bouckaert, R.R.: Properties of Bayesian belief network learning algorithms. In: Tenth International Conference on Uncertainty in Artificial Intelligence, pp. 102\u2013109 (1994). https:\/\/doi.org\/10.1016\/B978-1-55860-332-5.50018-3","DOI":"10.1016\/B978-1-55860-332-5.50018-3"},{"key":"18_CR5","unstructured":"Bouckaert, R.: Bayesian belief networks: from construction to inference. Ph.D. thesis, University of Utrecht, Utrecht, Netherlands (1995). https:\/\/core.ac.uk\/download\/pdf\/39700264.pdf"},{"key":"18_CR6","unstructured":"Chickering, D.M., Meek, C.: Finding optimal Bayesian networks. In: Eighteenth Conference on Uncertainty in Artificial Intelligence, pp. 94\u2013102 (2002)"},{"key":"18_CR7","unstructured":"Constantinou, A.C.: The bayesys user manual (2019). http:\/\/constantinou.info\/downloads\/bayesys\/bayesys_manual.pdf. bayesian Artificial Intelligence Research Lab, MiNDS Group, Queen Mary University of London"},{"key":"18_CR8","doi-asserted-by":"publisher","first-page":"124845","DOI":"10.1109\/ACCESS.2020.3006472","volume":"8","author":"AC Constantinou","year":"2020","unstructured":"Constantinou, A.C.: Learning Bayesian networks that enable full propagation of evidence. IEEE Access 8, 124845\u2013124856 (2020). https:\/\/doi.org\/10.1109\/ACCESS.2020.3006472","journal-title":"IEEE Access"},{"issue":"8","key":"18_CR9","doi-asserted-by":"publisher","first-page":"3385","DOI":"10.1007\/s10115-023-01858-x","volume":"65","author":"AC Constantinou","year":"2023","unstructured":"Constantinou, A.C., Guo, Z., Kitson, N.K.: The impact of prior knowledge on causal structure learning. Knowl. Inf. Syst. 65(8), 3385\u20133434 (2023). https:\/\/doi.org\/10.1007\/s10115-023-01858-x","journal-title":"Knowl. Inf. Syst."},{"key":"18_CR10","doi-asserted-by":"publisher","unstructured":"Constantinou, A.C., Liu, Y., Kitson, N.K., Chobtham, K., Guo, Z.: Effective and efficient structure learning with pruning and model averaging strategies. Int. J. Approx. Reasoning 151(C), 292\u2013321 (2022). https:\/\/doi.org\/10.1016\/j.ijar.2022.09.016","DOI":"10.1016\/j.ijar.2022.09.016"},{"key":"18_CR11","doi-asserted-by":"publisher","unstructured":"Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1), 21\u201327 (1967). https:\/\/doi.org\/10.1109\/TIT.1967.1053964, https:\/\/ieeexplore.ieee.org\/document\/1053964","DOI":"10.1109\/TIT.1967.1053964"},{"key":"18_CR12","doi-asserted-by":"crossref","unstructured":"Davey Smith, G., Ebrahim, S.: \u2018Mendelian randomization\u2019: can genetic epidemiology contribute to understanding environmental determinants of disease? Int. J. Epidemiol. 32(1), 1\u201322 (2003)","DOI":"10.1093\/ije\/dyg070"},{"issue":"17","key":"18_CR13","doi-asserted-by":"publisher","first-page":"6275","DOI":"10.3390\/ijms21176275","volume":"21","author":"U Galicia-Garcia","year":"2020","unstructured":"Galicia-Garcia, U., et al.: Pathophysiology of type 2 diabetes mellitus. Int. J. Mol. Sci. 21(17), 6275 (2020)","journal-title":"Int. J. Mol. Sci."},{"key":"18_CR14","doi-asserted-by":"publisher","unstructured":"Genewein, T., et al.: Algorithms for causal reasoning in probability trees. arXiv preprint arXiv:2010.12237 (2020). https:\/\/doi.org\/10.48550\/arXiv.2010.12237","DOI":"10.48550\/arXiv.2010.12237"},{"key":"18_CR15","doi-asserted-by":"publisher","unstructured":"Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian networks: the combination of knowledge and statistical data. Mach. Learn. 20, 197\u2013243 (1995). https:\/\/doi.org\/10.1023\/A:1022623210503","DOI":"10.1023\/A:1022623210503"},{"issue":"6","key":"18_CR16","doi-asserted-by":"publisher","first-page":"1243","DOI":"10.2337\/dc20-2834","volume":"44","author":"AG Jones","year":"2021","unstructured":"Jones, A.G., McDonald, T.J., Shields, B.M., Hagopian, W., Hattersley, A.T.: Latent autoimmune diabetes of adults (lada) is likely to represent a mixed population of autoimmune (type 1) and nonautoimmune (type 2) diabetes. Diabetes Care 44(6), 1243\u20131251 (2021)","journal-title":"Diabetes Care"},{"issue":"11","key":"18_CR17","doi-asserted-by":"publisher","first-page":"558","DOI":"10.1145\/368996.369025","volume":"5","author":"AB Kahn","year":"1962","unstructured":"Kahn, A.B.: Topological sorting of large networks. Commun. ACM 5(11), 558\u2013562 (1962). https:\/\/doi.org\/10.1145\/368996.369025","journal-title":"Commun. ACM"},{"key":"18_CR18","doi-asserted-by":"crossref","unstructured":"Kulkarni, A., Thool, A.R., Daigavane, S., Aditi, S.: Understanding the clinical relationship between diabetic retinopathy, nephropathy, and neuropathy: a comprehensive review. Cureus 16(3) (2024)","DOI":"10.7759\/cureus.56674"},{"key":"18_CR19","doi-asserted-by":"publisher","unstructured":"LaValley, M.P.: Logistic regression. Circulation 117(18), 2395\u20132399 (2008). https:\/\/doi.org\/10.1161\/CIRCULATIONAHA.106.682658, https:\/\/www.ahajournals.org\/doi\/full\/10.1161\/circulationaha.106.682658","DOI":"10.1161\/CIRCULATIONAHA.106.682658"},{"issue":"1","key":"18_CR20","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1038\/s41392-023-01400-z","volume":"8","author":"Y Li","year":"2023","unstructured":"Li, Y., et al.: Diabetic vascular diseases: molecular mechanisms and therapeutic strategies. Sig. Transduct. Target. Ther. 8(1), 152 (2023)","journal-title":"Sig. Transduct. Target. Ther."},{"key":"18_CR21","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btad547","author":"B Muzellec","year":"2023","unstructured":"Muzellec, B., Telenczuk, M., Cabeli, V., Andreux, M.: PydeSeq2: a python package for bulk RNA-Seq differential expression analysis. Bioinformatics (2023). https:\/\/doi.org\/10.1093\/bioinformatics\/btad547","journal-title":"Bioinformatics"},{"key":"18_CR22","doi-asserted-by":"publisher","DOI":"10.1038\/s41366-020-00694-1","author":"C Osinski","year":"2021","unstructured":"Osinski, C., et al.: Type 2 diabetes is associated with impaired jejunal enteroendocrine GLP-1 cell lineage in human obesity. Int. J. Obes. (2021). https:\/\/doi.org\/10.1038\/s41366-020-00694-1","journal-title":"Int. J. Obes."},{"issue":"1","key":"18_CR23","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1038\/s41366-020-00694-1","volume":"45","author":"C Osinski","year":"2021","unstructured":"Osinski, C., et al.: Type 2 diabetes is associated with impaired jejunal enteroendocrine glp-1 cell lineage in human obesity. Int. J. Obes. 45(1), 170\u2013183 (2021)","journal-title":"Int. J. Obes."},{"issue":"3","key":"18_CR24","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1111\/j.1753-0407.2012.00224.x","volume":"4","author":"Q Qi","year":"2012","unstructured":"Qi, Q., Hu, F.B.: Genetics of type 2 diabetes in European populations. J. Diabetes 4(3), 203\u2013212 (2012)","journal-title":"J. Diabetes"},{"issue":"18","key":"18_CR25","doi-asserted-by":"publisher","first-page":"3508","DOI":"10.1093\/hmg\/ddp294","volume":"18","author":"Q Qi","year":"2009","unstructured":"Qi, Q., et al.: Common variants in kcnq1 are associated with type 2 diabetes and impaired fasting glucose in a Chinese Han population. Hum. Mol. Genet. 18(18), 3508\u20133515 (2009)","journal-title":"Hum. Mol. Genet."},{"key":"18_CR26","doi-asserted-by":"publisher","unstructured":"Rigatti, S.J.: Random forest. J. Insurance Med. 47(1), 31\u201339 (2017). https:\/\/doi.org\/10.17849\/insm-47-01-31-39.1, https:\/\/pubmed.ncbi.nlm.nih.gov\/28836909\/","DOI":"10.17849\/insm-47-01-31-39.1"},{"key":"18_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s11658-016-0002-4","volume":"21","author":"Z Sepehri","year":"2016","unstructured":"Sepehri, Z., Kiani, Z., Nasiri, A.A., Kohan, F.: Toll-like receptor 2 and type 2 diabetes. Cell. Mol. Biol. Lett. 21, 1\u20139 (2016)","journal-title":"Cell. Mol. Biol. Lett."},{"key":"18_CR28","doi-asserted-by":"publisher","unstructured":"Suthaharan, S., Suthaharan, S.: Decision tree learning. In: Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning, pp. 237\u2013269 (2016). https:\/\/doi.org\/10.1007\/978-1-4899-7641-3_10","DOI":"10.1007\/978-1-4899-7641-3_10"},{"issue":"1","key":"18_CR29","doi-asserted-by":"publisher","first-page":"638","DOI":"10.1093\/nar\/gkac1000","volume":"51","author":"D Szklarczyk","year":"2023","unstructured":"Szklarczyk, D., et al.: The string database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 51(1), 638\u2013646 (2023)","journal-title":"Nucleic Acids Res."},{"issue":"8","key":"18_CR30","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1002\/cbin.12189","volume":"48","author":"R Taheri","year":"2024","unstructured":"Taheri, R., Mokhtari, Y., Yousefi, A.M., Bashash, D.: The pi3k\/akt signaling axis and type 2 diabetes mellitus (t2dm): from mechanistic insights into possible therapeutic targets. Cell Biol. Int. 48(8), 1049\u20131068 (2024)","journal-title":"Cell Biol. Int."},{"issue":"2","key":"18_CR31","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1137\/0201010","volume":"1","author":"RE Tarjan","year":"1972","unstructured":"Tarjan, R.E.: Depth-first search and linear graph algorithms. SIAM J. Comput. 1(2), 146\u2013160 (1972). https:\/\/doi.org\/10.1137\/0201010","journal-title":"SIAM J. Comput."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-00652-3_18","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T04:51:26Z","timestamp":1763614286000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-00652-3_18"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,20]]},"ISBN":["9783032006516","9783032006523"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-00652-3_18","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,8,20]]},"assertion":[{"value":"20 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIiH","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on AI in Healthcare","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cambridge","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aiih2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/aiih.cc\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}