{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T03:44:40Z","timestamp":1752551080105,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819619061"},{"type":"electronic","value":"9789819619078"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-1907-8_10","type":"book-chapter","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T19:54:58Z","timestamp":1740426898000},"page":"103-114","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Causal Inference-Based Feature Selection Method for Identifying Alzheimer's Disease Biomarker"],"prefix":"10.1007","author":[{"given":"Jingxin","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhao","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaolong","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuchen","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caihua","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaowang","family":"Lan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"key":"10_CR1","doi-asserted-by":"crossref","unstructured":"Sharma, P., Singh, M.: An ongoing journey of chalcone analogues as single and multi-target ligands in the field of Alzheimer\u2019s disease: a review with structural aspects. Life Sci. 121568 (2023)","DOI":"10.1016\/j.lfs.2023.121568"},{"key":"10_CR2","unstructured":"World Health Organization: Dementia. Available at: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/dementia. Accessed 05 April 2023"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Dong, C.M., et al.: Early Detection of Amyloid \u03b2 Pathology in Alzheimer\u2019s Disease by Molecular MRI. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, EMBC, pp. 1100\u20131103. IEEE (2020)","DOI":"10.1109\/EMBC44109.2020.9176013"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Dong, C.M., et al.: A computational Monte Carlo simulation strategy to determine the temporal ordering of abnormal age onset among biomarkers of Alzheimer\u2019s disease. IEEE\/ACM Trans Comput Biol Bioinform 19(5), 2613\u20132622 (2022)","DOI":"10.1109\/TCBB.2021.3106939"},{"issue":"11","key":"10_CR5","doi-asserted-by":"publisher","first-page":"1984","DOI":"10.1212\/01.WNL.0000129697.01779.0A","volume":"62","author":"P Tiraboschi","year":"2004","unstructured":"Tiraboschi, P., Hansen, L.A.: The importance of neuritic plaques and tangles to the development and evolution of AD. Neurology 62(11), 1984\u20131989 (2004)","journal-title":"Neurology"},{"key":"10_CR6","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.inffus.2020.09.002","volume":"66","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Wang, S., Xia, K., Jiang, Y., Qian, P.: Alzheimer\u2019s disease neuroimaging initiative: Alzheimer\u2019s disease multiclass diagnosis via multimodal neuroimaging embedding feature selection and fusion. Information Fusion. 66, 170\u2013183 (2021)","journal-title":"Information Fusion."},{"key":"10_CR7","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.853294","volume":"10","author":"C Kavitha","year":"2022","unstructured":"Kavitha, C., Mani, V., Srividhya, S.R., Khalaf, O.I., Tavera Romero, C.A.: Early-stage Alzheimer\u2019s disease prediction using machine learning models. Front. Public Health 10, 853294 (2022)","journal-title":"Front. Public Health"},{"issue":"4","key":"10_CR8","doi-asserted-by":"publisher","first-page":"2353","DOI":"10.3390\/app13042353","volume":"13","author":"P Paplomatas","year":"2023","unstructured":"Paplomatas, P., Krokidis, M.G., Vlamos, P., Vrahatis, A.G.: An ensemble feature selection approach for analysis and modeling of transcriptome data in Alzheimer\u2019s disease. Appl. Sci. 13(4), 2353 (2023)","journal-title":"Appl. Sci."},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Sharma, N., Singh, A.N.: Exploring biomarkers for Alzheimer\u2019s disease. J. Clinical and Diagnostic Res. JCDR. 10(7), KE01-KE06 (2016)","DOI":"10.7860\/JCDR\/2016\/18828.8166"},{"issue":"6","key":"10_CR10","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1111\/joim.12816","volume":"284","author":"K Blennow","year":"2018","unstructured":"Blennow, K., Zetterberg, H.: Biomarkers for Alzheimer\u2019s disease: current status and prospects for the future. J. Intern. Med. 284(6), 643\u2013663 (2018)","journal-title":"J. Intern. Med."},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Kaur, A., Guleria, K., Trivedi, N.K.: Feature selection in machine learning: methods and comparison. In: 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE), pp. 789\u2013795. IEEE (2021)","DOI":"10.1109\/ICACITE51222.2021.9404623"},{"issue":"11","key":"10_CR12","first-page":"1855","volume":"6","author":"L Wolf","year":"2005","unstructured":"Wolf, L., Shashua, A., Geman, D.: Feature selection for unsupervised and supervised inference: the emergence of sparsity in a weight-based approach. Journal of Machine Learning Research. Res. 6(11), 1855\u20131887 (2005)","journal-title":"Journal of Machine Learning Research. Res."},{"issue":"1","key":"10_CR13","doi-asserted-by":"publisher","first-page":"3","DOI":"10.2478\/cait-2019-0001","volume":"19","author":"B Venkatesh","year":"2019","unstructured":"Venkatesh, B., Anuradha, J.: A review of feature selection and its methods. Cybernetics and Information Technologies 19(1), 3\u201326 (2019)","journal-title":"Cybernetics and Information Technologies"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez-Maro\u00f1o, N., Alonso-Betanzos, A., Tombilla-Sanrom\u00e1n, M.: Filter methods for feature selection \u2013 a comparative study. In: International Conference on Intelligent Data Engineering and Automated Learning, pp. 178\u2013187. Springer, Berlin, Heidelberg (2007)","DOI":"10.1007\/978-3-540-77226-2_19"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Suto, J., Oniga, S., Pop Sitar, P.: Comparison of wrapper and filter feature selection algorithms on human activity recognition. In: 2016 6th International Conference on Computers Communications and Control (ICCCC), pp. 124\u2013129. IEEE (2016)","DOI":"10.1109\/ICCCC.2016.7496749"},{"key":"10_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112873","volume":"140","author":"C Park","year":"2020","unstructured":"Park, C., Ha, J., Park, S.: Prediction of Alzheimer\u2019s disease based on deep neural network by integrating gene expression and DNA methylation dataset. Expert Syst. Appl. 140, 112873 (2020)","journal-title":"Expert Syst. Appl."},{"key":"10_CR17","doi-asserted-by":"crossref","unstructured":"Cunningham, S.: Causal Inference: The Mixtape. Yale University Press (2021)","DOI":"10.12987\/9780300255881"},{"key":"10_CR18","doi-asserted-by":"crossref","unstructured":"Pearl, J.: An Introduction to Causal Inference. Int. J. Biostat. 6(2) (2010)","DOI":"10.2202\/1557-4679.1203"},{"issue":"3","key":"10_CR19","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1097\/EDE.0000000000000078","volume":"25","author":"ML Petersen","year":"2014","unstructured":"Petersen, M.L., van der Laan, M.J.: Causal models and learning from data: integrating causal modeling and statistical estimation. Epidemiology 25(3), 418\u2013426 (2014)","journal-title":"Epidemiology"},{"issue":"6","key":"10_CR20","doi-asserted-by":"publisher","first-page":"1112","DOI":"10.1093\/ejcts\/ezy167","volume":"53","author":"U Benedetto","year":"2018","unstructured":"Benedetto, U., Head, S.J., Angelini, G.D., Blackstone, E.H.: Statistical primer: propensity score matching and its alternatives. Eur. J. Cardio-Thorac. Surg. 53(6), 1112\u20131117 (2018)","journal-title":"Eur. J. Cardio-Thorac. Surg."},{"issue":"3","key":"10_CR21","doi-asserted-by":"publisher","first-page":"709","DOI":"10.1177\/0962280219888745","volume":"29","author":"S Zhao","year":"2020","unstructured":"Zhao, S., van Dyk, D.A., Imai, K.: Propensity score-based methods for causal inference in observational studies with non-binary treatments. Stat. Methods Med. Res. 29(3), 709\u2013727 (2020)","journal-title":"Stat. Methods Med. Res."},{"key":"10_CR22","unstructured":"Zhao, S., Van Dyk, D.A., Imai, K.: Causal inference in observational studies with non-binary treatments. In: arXiv preprint arXiv:1309.6361 (2013)"},{"key":"10_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2020.05.017","volume":"534","author":"F Thabtah","year":"2020","unstructured":"Thabtah, F., Kamalov, F., Hammoud, S., Shahamiri, S.R.: Least loss: a simplified filter method for feature selection. Inf. Sci. 534, 1\u201315 (2020)","journal-title":"Inf. Sci."},{"issue":"19","key":"10_CR24","doi-asserted-by":"publisher","first-page":"7354","DOI":"10.3390\/ijms21197354","volume":"21","author":"A Sferra","year":"2020","unstructured":"Sferra, A., Nicita, F., Bertini, E.: Microtubule dysfunction: a common feature of neurodegenerative diseases. Int. J. Mol. Sci. 21(19), 7354 (2020)","journal-title":"Int. J. Mol. Sci."},{"key":"10_CR25","doi-asserted-by":"crossref","unstructured":"Baquero, J., et al.: Nuclear Tau, p53 and Pin1 Regulate PARN-Mediated Deadenylation and Gene Expression. Frontiers in Molecular Neuroscience 12, p. 242 (2019)","DOI":"10.3389\/fnmol.2019.00242"},{"key":"10_CR26","doi-asserted-by":"crossref","unstructured":"Yasukawa, T., et al.: NRBP1-containing CRL2\/CRL4A regulates amyloid \u03b2 production by targeting BRI2 and BRI3 for degradation. Cell Reports 30(10), 3478\u20133491 (2020)","DOI":"10.1016\/j.celrep.2020.02.059"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Dai, Z., et al.: Structural insights into the Ubiquitylation strategy of the oligomeric CRL2FEM1B E3 ubiquitin ligase. The EMBO Journal 43(6), 1089\u20131109 (2024)","DOI":"10.1038\/s44318-024-00047-y"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Cheng, J., et al.: The Emerging Role for Cullin 4 Family of E3 Ligases in Tumorigenesis. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer. 1871(1), pp. 138\u2013159 (2019)","DOI":"10.1016\/j.bbcan.2018.11.007"},{"issue":"11","key":"10_CR29","doi-asserted-by":"publisher","first-page":"2172","DOI":"10.3390\/genes13112172","volume":"13","author":"MN Ivanov","year":"2022","unstructured":"Ivanov, M.N., Stoyanov, D.S., Pavlov, S.P., Tonchev, A.B.: Distribution, function, and expression of the Apelinergic system in the healthy and diseased mammalian brain. Genes 13(11), 2172 (2022)","journal-title":"Genes"},{"issue":"13","key":"10_CR30","doi-asserted-by":"publisher","first-page":"1766","DOI":"10.1093\/bioinformatics\/bts238","volume":"28","author":"VA Huynh-Thu","year":"2012","unstructured":"Huynh-Thu, V.A., Saeys, Y., Wehenkel, L., Geurts, P.: Statistical interpretation of machine learning-based feature importance scores for biomarker discovery. Bioinformatics 28(13), 1766\u20131774 (2012)","journal-title":"Bioinformatics"},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, Y.H., Jin, M., Li, J., Kong, X.: Identifying circulating miRNA biomarkers for early diagnosis and monitoring of lung cancer. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease 1866(10), 165847 (2020)","DOI":"10.1016\/j.bbadis.2020.165847"},{"key":"10_CR32","unstructured":"Grandini, M., Bagli, E., Visani, G.: Metrics for Multi-Class Classification: An Overview. arXiv preprint arXiv:2008.05756 (2020)"}],"container-title":["Communications in Computer and Information Science","Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-1907-8_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T19:55:10Z","timestamp":1740426910000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-1907-8_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819619061","9789819619078"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-1907-8_10","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"25 February 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Applied Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhenzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 November 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 November 2024","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":"icai12024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icai.org.cn\/2024\/Organization.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}