@inproceedings{132fdd7d0d1e4725a3646c6865c91256,
title = "Structured sparse kernel learning for imaging genetics based alzheimer{\textquoteright}s disease diagnosis",
abstract = "A kernel-learning based method is proposed to integrate multimodal imaging and genetic data for Alzheimer{\textquoteright}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{\textquoteright}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.",
author = "Jailin Peng and Le An and Xiaofeng Zhu and Yan Jin and Dinggang Shen",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing AG 2016.",
year = "2016",
doi = "10.1007/978-3-319-46723-8_9",
language = "English (US)",
isbn = "9783319467221",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "70--78",
editor = "Gozde Unal and Sebastian Ourselin and Leo Joskowicz and Sabuncu, {Mert R.} and William Wells",
booktitle = "Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings",
}