A Novel Structure-Aware Sparse Learning Algorithm for Brain Imaging Genetics

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• 作者：Lei Du (20)
  Jingwen Yan (20) (21)
  Sungeun Kim (20)
  Shannon L. Risacher (20)
  Heng Huang (22)
  Mark Inlow (23)
  Jason H. Moore (24)
  Andrew J. Saykin (20)
  Li Shen (20) (21)
  
• 刊名：Lecture Notes in Computer Science
• 出版年：2014
• 出版时间：2014
• 年：2014
• 卷：8675
• 期：1
• 页码：329-336
• 全文大小：413 KB
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• 作者单位：Lei Du (20)
  Jingwen Yan (20) (21)
  Sungeun Kim (20)
  Shannon L. Risacher (20)
  Heng Huang (22)
  Mark Inlow (23)
  Jason H. Moore (24)
  Andrew J. Saykin (20)
  Li Shen (20) (21)
  
  20. Radiology and Imaging Sciences, Indiana University School of Medicine, IN, USA
  21. School of Informatics and Computing, Indiana University Indianapolis, IN, USA
  22. Computer Science and Engineering, University of Texas at Arlington, TX, USA
  23. Mathematics, Rose-Hulman Institute of Technology, IN, USA
  24. Genetics, Geisel School of Medicine, Dartmouth College, NH, USA
  
• ISSN：1611-3349

文摘

Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. Most existing SCCA algorithms are designed using the soft threshold strategy, which assumes that the features in the data are independent from each other. This independence assumption usually does not hold in imaging genetic data, and thus inevitably limits the capability of yielding optimal solutions. We propose a novel structure-aware SCCA (denoted as S2CCA) algorithm to not only eliminate the independence assumption for the input data, but also incorporate group-like structure in the model. Empirical comparison with a widely used SCCA implementation, on both simulated and real imaging genetic data, demonstrated that S2CCA could yield improved prediction performance and biologically meaningful findings.