Deep Network-Based Feature Selection for Imaging Genetics: Application to Identifying Biomarkers for Parkinson's Disease

Mansu Kim, Ji Hye Won, Jisu Hong, Junmo Kwon, Hyunjin Park, Li Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Imaging genetics is a methodology for discovering associations between imaging and genetic variables. Many studies adopted sparse models such as sparse canonical correlation analysis (SCCA) for imaging genetics. These methods are limited to modeling the linear imaging genetics relationship and cannot capture the non-linear high-level relationship between the explored variables. Deep learning approaches are underexplored in imaging genetics, compared to their great successes in many other biomedical domains such as image segmentation and disease classification. In this work, we proposed a deep learning model to select genetic features that can explain the imaging features well. Our empirical study on simulated and real datasets demonstrated that our method outperformed the widely used SCCA method and was able to select important genetic features in a robust fashion. These promising results indicate our deep learning model has the potential to reveal new biomarkers to improve mechanistic understanding of the studied brain disorders.

Original languageEnglish (US)
Title of host publicationISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages1920-1923
Number of pages4
ISBN (Electronic)9781538693308
DOIs
StatePublished - Apr 2020
Event17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 - Iowa City, United States
Duration: Apr 3 2020Apr 7 2020

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2020-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
CountryUnited States
CityIowa City
Period4/3/204/7/20

Keywords

  • Imaging genetics
  • Parkinson's disease
  • deep learning
  • feature selection

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Kim, M., Won, J. H., Hong, J., Kwon, J., Park, H., & Shen, L. (2020). Deep Network-Based Feature Selection for Imaging Genetics: Application to Identifying Biomarkers for Parkinson's Disease. In ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging (pp. 1920-1923). [9098471] (Proceedings - International Symposium on Biomedical Imaging; Vol. 2020-April). IEEE Computer Society. https://doi.org/10.1109/ISBI45749.2020.9098471