ENGINE: Enhancing Neuroimaging and Genetic Information by Neural Embedding

Wonjun Ko, Wonsik Jung, Eunjin Jeon, Ahmad Wisnu Mulyadi, Heung Il Suk

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

1 Citation (Scopus)


Recently, deep learning, a branch of machine learning and data mining, has gained widespread acceptance in many applications thanks to its unprecedented successes. In this regard, pioneering studies employed deep learning frameworks for imaging genetics in virtue of their own representation caliber. But, existing approaches suffer from some limitations: (i) exploiting a simple concatenation strategy for joint analysis, (ii) a lack of extension to biomedical applications, and (iii) insufficient and inappropriate interpretations in the viewpoint of both data science and bio-neuroscience. In this work, we propose a novel deep learning framework to tackle the aforementioned issues simultaneously. Our proposed framework learns to effectively represent the neuroimaging and the genetic data jointly, and achieves state-of-the-art performance in its use for Alzheimer's disease and mild cognitive impairment identification. Further, unlike the existing methods in the literature, the framework allows learning the relation between imaging phenotypes and genotypes in a nonlinear way without any prior neuroscientific knowledge. To demonstrate the validity of our proposed framework, we conducted experiments on a publicly available dataset and analyzed the results from diverse perspectives. Based on our experimental results, we believe that the proposed framework has a great potential to give new insights and perspectives in deep learning-based imaging genetics studies.

Original languageEnglish
Title of host publicationProceedings - 21st IEEE International Conference on Data Mining, ICDM 2021
EditorsJames Bailey, Pauli Miettinen, Yun Sing Koh, Dacheng Tao, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665423984
Publication statusPublished - 2021
Event21st IEEE International Conference on Data Mining, ICDM 2021 - Virtual, Online, New Zealand
Duration: 2021 Dec 72021 Dec 10

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


Conference21st IEEE International Conference on Data Mining, ICDM 2021
Country/TerritoryNew Zealand
CityVirtual, Online


  • Data mining
  • deep learning
  • imaging genetics
  • magnetic resonance imaging
  • single nucleotide polymorphism

ASJC Scopus subject areas

  • Engineering(all)


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