{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Trinh M"],"funding":["NIA NIH HHS"],"pagination":["1332928"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11271260"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["3"],"pubmed_abstract":["Alzheimer's disease (AD) is affecting a growing number of individuals. As a result, there is a pressing need for accurate and early diagnosis methods. This study aims to achieve this goal by developing an optimal data analysis strategy to enhance computational diagnosis. Although various modalities of AD diagnostic data are collected, past research on computational methods of AD diagnosis has mainly focused on using single-modal inputs. We hypothesize that integrating, or \"fusing,\" various data modalities as inputs to prediction models could enhance diagnostic accuracy by offering a more comprehensive view of an individual's health profile. However, a potential challenge arises as this fusion of multiple modalities may result in significantly higher dimensional data. We hypothesize that em"],"journal":["Frontiers in dementia"],"pubmed_title":["Alzheimer's disease detection using data fusion with a deep supervised encoder."],"pmcid":["PMC11271260"],"funding_grant_id":["F30 AG079610","P30 AG062677","P30 AG066514","P30 AG066515","P30 AG066518","P30 AG066519","P30 AG062715","P20 AG068053","P30 AG072931","P30 AG066462","P30 AG072975","P20 AG068077","P30 AG072976","P30 AG072977","P30 AG066444","P30 AG072972","P30 AG072973","P30 AG066468","P30 AG072978","P30 AG072979","R01 AG079280","P30 AG072958","P30 AG072959","P30 AG062421","P30 AG062422","P30 AG066546","P30 AG066506","P30 AG066507","P30 AG066508","P30 AG062429","P30 AG066509","P30 AG066530","P20 AG068082","P30 AG066511","P30 AG066512","U24 AG072122","P20 AG068024","P30 AG072946","P30 AG072947"],"pubmed_authors":["Ren Y","Shahbaba R","Trinh M","Stark C"],"additional_accession":[]},"is_claimable":false,"name":"Alzheimer's disease detection using data fusion with a deep supervised encoder.","description":"Alzheimer's disease (AD) is affecting a growing number of individuals. As a result, there is a pressing need for accurate and early diagnosis methods. This study aims to achieve this goal by developing an optimal data analysis strategy to enhance computational diagnosis. Although various modalities of AD diagnostic data are collected, past research on computational methods of AD diagnosis has mainly focused on using single-modal inputs. We hypothesize that integrating, or \"fusing,\" various data modalities as inputs to prediction models could enhance diagnostic accuracy by offering a more comprehensive view of an individual's health profile. However, a potential challenge arises as this fusion of multiple modalities may result in significantly higher dimensional data. We hypothesize that em","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024","modification":"2025-04-19T07:06:58.389Z","creation":"2025-04-19T07:06:58.389Z"},"accession":"S-EPMC11271260","cross_references":{"pubmed":["39055313"],"doi":["10.3389/frdem.2024.1332928"]}}