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Given that there is currently no cure, it is critical to detect AD in its early stage during the disease progression. Recently, many statistical learning methods have been presented to identify cognitive decline with temporal data, but few of these methods integrate heterogeneous phenotype and genetic information together to improve the accuracy of prediction. In addition, many of these models are often unable to handle incomplete temporal data; this often manifests itself in the removal of records to ensure consistency in the number of records across participants.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>To address these issues, in this work we propose a novel approach to integrate the genetic data and the longitudinal phenotype data to learn a fixed-length \u201cenriched\u201d biomarker representation derived from the temporal heterogeneous neuroimaging records. Armed with this enriched representation, as a fixed-length vector per participant, conventional machine learning models can be used to predict clinical outcomes associated with AD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed method shows improved prediction performance when applied to data derived from Alzheimer\u2019s Disease Neruoimaging Initiative cohort. In addition, our approach can be easily interpreted to allow for the identification and validation of biomarkers associated with cognitive decline.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-024-02455-w","type":"journal-article","created":{"date-parts":[[2024,5,28]],"date-time":"2024-05-28T09:03:06Z","timestamp":1716886986000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Learning semi-supervised enrichment of longitudinal imaging-genetic data for improved prediction of cognitive decline"],"prefix":"10.1186","volume":"24","author":[{"given":"Hoon","family":"Seo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lodewijk","family":"Brand","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5986-7413","authenticated-orcid":false,"given":"Hua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"name":"for the\u00a0Alzheimer\u2019s\u00a0Disease\u00a0Neuroimaging\u00a0Initiative","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,5,28]]},"reference":[{"key":"2455_CR1","unstructured":"World Health Organization.\u00a0Global action plan on the public health response to dementia\u00a02017-2025. 2017; 2020.\u00a0https:\/\/iris.who.int\/bitstream\/handle\/10665\/259615\/9789241513487-eng.pdf?sequence=1,\u00a0https:\/\/www.who.int\/publications\/i\/item\/global-action-plan-on-the-public-health-response-to-dementia-2017---2025."},{"issue":"1","key":"2455_CR2","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.biopsych.2014.05.006","volume":"77","author":"CM Karch","year":"2015","unstructured":"Karch CM, Goate AM. 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