Incorporating spatial–anatomical similarity into the VGWAS framework for AD biomarker detection | Zendy

Meiyan Huang | Zendy; Yuwei Yu | Zendy; Wei Yang | Zendy; Qianjin Feng | Zendy

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Incorporating spatial–anatomical similarity into the VGWAS framework for AD biomarker detection

Author(s) -

Meiyan Huang,

Yuwei Yu,

Wei Yang,

Qianjin Feng

Publication year - 2019

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btz401

Subject(s) - computer science , artificial intelligence , imaging genetics , voxel , cluster analysis , similarity (geometry) , neuroimaging , pattern recognition (psychology) , machine learning , computational biology , data mining , image (mathematics) , medicine , biology , psychiatry

The detection of potential biomarkers of Alzheimer's disease (AD) is crucial for its early prediction, diagnosis and treatment. Voxel-wise genome-wide association study (VGWAS) is a commonly used method in imaging genomics and usually applied to detect AD biomarkers in imaging and genetic data. However, existing VGWAS methods entail large computational cost and disregard spatial correlations within imaging data. A novel method is proposed to solve these issues.

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