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.
This study was partially supported by U.S. NSF IIS 1836945, IIS 1836938, DBI 1836866, IIS 1845666, IIS 1852606, IIS 1838627, IIS 1837956, and NIH AG049371. NIH AG056782.
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Zhang, Y., Zhan, L., Thompson, P.M., Huang, H. (2019). Biological Knowledge Guided Deep Neural Network for Brain Genotype-Phenotype Association Study. In: Zhu, D., et al. Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy. MBIA MFCA 2019 2019. Lecture Notes in Computer Science(), vol 11846. Springer, Cham. https://doi.org/10.1007/978-3-030-33226-6_10
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