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  • Guigui, N. (author)

Network Regularization in Imaging Genetics Improves Prediction Performances and Model Interpretability on Alzheimer’s Disease

  • Article/chapterEnglish2019

Publisher, publication year, extent ...

  • IEEE,2019
  • printrdacarrier

Numbers

  • LIBRIS-ID:oai:DiVA.org:umu-164661
  • https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-164661URI
  • https://doi.org/10.1109/ISBI.2019.8759593DOI

Supplementary language notes

  • Language:English
  • Summary in:English

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  • Subject category:ref swepub-contenttype
  • Subject category:kon swepub-publicationtype

Notes

  • Imaging genetics is a growing popular research avenue which aims to find genetic variants associated with quantitative phenotypes that characterize a disease. In this work, we combine structural MRI with genetic data structured by prior knowledge of interactions in a Canonical Correlation Analysis (CCA) model with graph regularization. This results in improved prediction performance and yields a more interpretable model.

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Added entries (persons, corporate bodies, meetings, titles ...)

  • Philippe, C. (author)
  • Gloaguen, A. (author)
  • Karkar, S. (author)
  • Guillemot, V. (author)
  • Löfstedt, TommyUmeå universitet,Radiofysik(Swepub:umu)toklot02 (author)
  • Frouin, V. (author)
  • Umeå universitetRadiofysik (creator_code:org_t)

Related titles

  • In:2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019): IEEE, s. 1403-14069781538636411

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