Learning Semi-Supervised Representation Enrichment Using Longitudinal Imaging-Genetic Data

Seo, Hoon; Brand, Lodewijk; Wang, Hua

doi:10.1109/bibm49941.2020.9313310

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) 2020

DOI: 10.1109/bibm49941.2020.9313310

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Learning Semi-Supervised Representation Enrichment Using Longitudinal Imaging-Genetic Data

Hoon Seo

Lodewijk Brand

Hua Wang

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2024

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Learning semi-supervised enrichment of longitudinal imaging-genetic data for improved prediction of cognitive decline

Seo,

Brand,

Wang

2024

BMC Med Inform Decis Mak

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Background Alzheimer’s Disease (AD) is a progressive memory disorder that causes irreversible cognitive decline. 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. Results 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 “enriched” 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. Conclusion The proposed method shows improved prediction performance when applied to data derived from Alzheimer’s 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.

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Learning semi-supervised enrichment of longitudinal imaging-genetic data for improved prediction of cognitive decline

Seo,

Brand,

Wang

2024

BMC Med Inform Decis Mak

Self Cite

View full text Add to dashboard Cite

show abstract

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Learning Semi-Supervised Representation Enrichment Using Longitudinal Imaging-Genetic Data

Cited by 1 publication

References 13 publications

Learning semi-supervised enrichment of longitudinal imaging-genetic data for improved prediction of cognitive decline

Learning semi-supervised enrichment of longitudinal imaging-genetic data for improved prediction of cognitive decline

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