Structured sparse kernel learning for imaging genetics based alzheimer’s disease diagnosis

Jailin Peng, Le An, Xiaofeng Zhu, Yan Jin, Dinggang Shen

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

18 Citations (Scopus)

Abstract

A kernel-learning based method is proposed to integrate multimodal imaging and genetic data for Alzheimer’s disease (AD) diagnosis. To facilitate structured feature learning in kernel space,we represent each feature with a kernel and then group kernels according to modalities. In view of the highly redundant features within each modality and also the complementary information across modalities,we introduce a novel structured sparsity regularizer for feature selection and fusion,which is different from conventional lasso and group lasso based methods. Specifically,we enforce a penalty on kernel weights to simultaneously select features sparsely within each modality and densely combine different modalities. We have evaluated the proposed method using magnetic resonance imaging (MRI) and positron emission tomography (PET),and single-nucleotide polymorphism (SNP) data of subjects from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The effectiveness of our method is demonstrated by both the clearly improved prediction accuracy and the discovered brain regions and SNPs relevant to AD.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsGozde Unal, Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells
PublisherSpringer Verlag
Pages70-78
Number of pages9
ISBN (Print)9783319467221
DOIs
Publication statusPublished - 2016
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9901 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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