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Structured sparsity regularized multiple kernel learning for Alzheimer's disease diagnosis.

Jialin Peng1,2,3, Xiaofeng Zhu3, Ye Wang1

  • 1College of Computer Science and Technology, Huaqiao University, Xiamen, China.

Pattern Recognition
|March 16, 2019
PubMed
Summary
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This study introduces a new method for Alzheimer's disease (AD) diagnosis by fusing brain imaging and genetic data. The approach enhances diagnostic accuracy by effectively selecting and integrating features from multiple data types.

Area of Science:

  • Neuroimaging
  • Genetics
  • Machine Learning

Background:

  • Multimodal data fusion offers advantages over single-modality analysis for complex diseases.
  • Alzheimer's disease (AD) diagnosis can benefit from integrating diverse data sources like imaging and genetics.

Purpose of the Study:

  • To develop a novel method for Alzheimer's disease (AD) diagnosis by fusing multi-modality imaging and genetic data.
  • To leverage structured sparsity with a novel regularizer for effective feature selection and fusion.

Main Methods:

  • A structured sparsity regularized multiple kernel learning method using the ℓ1,p-norm (p>1) was designed.
  • Kernels were grouped by modality, enabling data-driven learning of an optimal combined kernel representation.
  • The method promotes sparse feature selection within modalities and fusion across modalities.
Keywords:
Alzheimer’s disease diagnosisFeature selectionMultimodal featuresMultiple kernel learningStructured sparsity

Related Experiment Videos

Main Results:

  • The proposed method was evaluated on data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
  • Demonstrated improved prediction diagnosis for Alzheimer's disease.
  • Identified specific brain regions and single nucleotide polymorphisms (SNPs) relevant to AD.

Conclusions:

  • The novel multimodal data fusion method significantly improves Alzheimer's disease diagnosis.
  • The approach effectively integrates imaging and genetic data, highlighting key AD-related biomarkers.