Robust and discriminative brain genome association study

Xiaofeng Zhu, Dinggang Shen

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

    Abstract

    Brain Genome Association (BGA) study, which investigates the associations between brain structure/function (characterized by neuroimaging phenotypes) and genetic variations (characterized by Single Nucleotide Polymorphisms (SNPs)), is important in pathological analysis of neurological disease. However, the current BGA studies are limited as they did not explicitly consider the disease labels, source importance, and sample importance in their formulations. We address these issues by proposing a robust and discriminative BGA formulation. Specifically, we learn two transformation matrices for mapping two heterogeneous data sources (i.e., neuroimaging data and genetic data) into a common space, so that the samples from the same subject (but different sources) are close to each other, and also the samples with different labels are separable. In addition, we add a sparsity constraint on the transformation matrices to enable feature selection on both data sources. Furthermore, both sample importance and source importance are also considered in the formulation via adaptive parameter-free sample and source weightings. We have conducted various experiments, using Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, to test how well the neuroimaging phenotypes and SNPs can represent each other in the common space.

    Original languageEnglish
    Title of host publicationMedical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings
    EditorsDinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou
    PublisherSpringer Science and Business Media Deutschland GmbH
    Pages456-464
    Number of pages9
    ISBN (Print)9783030322502
    DOIs
    Publication statusPublished - 2019
    Event22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
    Duration: 2019 Oct 132019 Oct 17

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11767 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
    Country/TerritoryChina
    CityShenzhen
    Period19/10/1319/10/17

    Bibliographical note

    Publisher Copyright:
    © Springer Nature Switzerland AG 2019.

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • General Computer Science

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