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Biological Knowledge Guided Deep Neural Network for Brain Genotype-Phenotype Association Study

  • Yanfu Zhang
  • Liang Zhan
  • Paul M. Thompson
  • Heng HuangEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11846)

Abstract

Alzheimer’s Disease (AD) is the main cause for age-related dementia. Many machine learning methods have been proposed to identify important genetic bases which are associated to phenotypes indicating the progress of AD. However, the biological knowledge is seldom considered in spite of the success of previous research. Built upon neuroimaging high-throughput phenotyping techniques, a biological knowledge guided deep network is proposed in this paper, to study the genotype-phenotype associations. We organized the Single Nucleotide Polymorphisms (SNPs) according to linkage disequilibrium (LD) blocks, and designed a group 1-D convolutional layer assembling both local and global convolution operations, to process the structural features. The entire neural network is a cascade of group 1-D convolutional layer, 2-D sliding convolutional layer and a multi-layer perceptron. The experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data show that the proposed method outperforms related methods. A set of biologically meaningful LD groups is also identified for phenotype discovery, which is potentially helpful for disease diagnosis and drug design.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yanfu Zhang
    • 1
  • Liang Zhan
    • 1
  • Paul M. Thompson
    • 2
  • Heng Huang
    • 1
    Email author
  1. 1.Department of Electrical and Computer EngineeringUniversity of PittsburghPittsburghUSA
  2. 2.Imaging Genetics Center, Institute for Neuroimaging and InformaticsUniversity of Southern CaliforniaLos AngelesUSA

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