Integrative analysis of multi-dimensional imaging genomics data for Alzheimer's disease prediction

In this paper, we explore the effects of integrating multi-dimensional imaging genomics data for Alzheimer's disease (AD) prediction using machine learning approaches. Precisely, we compare our three recent proposed feature selection methods [i.e., multiple kernel learning (MKL), high-order gra...

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Main Authors:	Zhang, Ziming, Huang, Heng, Shen, Dinggang
Format:	Online
Language:	English
Published:	Frontiers Media S.A. 2014
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4201101/

id	pubmed-4201101
recordtype	oai_dc
spelling	pubmed-42011012014-11-03 Integrative analysis of multi-dimensional imaging genomics data for Alzheimer's disease prediction Zhang, Ziming Huang, Heng Shen, Dinggang Neuroscience In this paper, we explore the effects of integrating multi-dimensional imaging genomics data for Alzheimer's disease (AD) prediction using machine learning approaches. Precisely, we compare our three recent proposed feature selection methods [i.e., multiple kernel learning (MKL), high-order graph matching based feature selection (HGM-FS), sparse multimodal learning (SMML)] using four widely-used modalities [i.e., magnetic resonance imaging (MRI), positron emission tomography (PET), cerebrospinal fluid (CSF), and genetic modality single-nucleotide polymorphism (SNP)]. This study demonstrates the performance of each method using these modalities individually or integratively, and may be valuable to clinical tests in practice. Our experimental results suggest that for AD prediction, in general, (1) in terms of accuracy, PET is the best modality; (2) Even though the discriminant power of genetic SNP features is weak, adding this modality to other modalities does help improve the classification accuracy; (3) HGM-FS works best among the three feature selection methods; (4) Some of the selected features are shared by all the feature selection methods, which may have high correlation with the disease. Using all the modalities on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the best accuracies, described as (mean ± standard deviation)%, among the three methods are (76.2 ± 11.3)% for AD vs. MCI, (94.8 ± 7.3)% for AD vs. HC, (76.5 ± 11.1)% for MCI vs. HC, and (71.0 ± 8.4)% for AD vs. MCI vs. HC, respectively. Frontiers Media S.A. 2014-10-17 /pmc/articles/PMC4201101/ /pubmed/25368574 http://dx.doi.org/10.3389/fnagi.2014.00260 Text en Copyright © 2014 Zhang, Huang, Shen and The Alzheimer's Disease Neuroimaging Initiative. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Zhang, Ziming Huang, Heng Shen, Dinggang
spellingShingle	Zhang, Ziming Huang, Heng Shen, Dinggang Integrative analysis of multi-dimensional imaging genomics data for Alzheimer's disease prediction
author_facet	Zhang, Ziming Huang, Heng Shen, Dinggang
author_sort	Zhang, Ziming
title	Integrative analysis of multi-dimensional imaging genomics data for Alzheimer's disease prediction
title_short	Integrative analysis of multi-dimensional imaging genomics data for Alzheimer's disease prediction
title_full	Integrative analysis of multi-dimensional imaging genomics data for Alzheimer's disease prediction
title_fullStr	Integrative analysis of multi-dimensional imaging genomics data for Alzheimer's disease prediction
title_full_unstemmed	Integrative analysis of multi-dimensional imaging genomics data for Alzheimer's disease prediction
title_sort	integrative analysis of multi-dimensional imaging genomics data for alzheimer's disease prediction
description	In this paper, we explore the effects of integrating multi-dimensional imaging genomics data for Alzheimer's disease (AD) prediction using machine learning approaches. Precisely, we compare our three recent proposed feature selection methods [i.e., multiple kernel learning (MKL), high-order graph matching based feature selection (HGM-FS), sparse multimodal learning (SMML)] using four widely-used modalities [i.e., magnetic resonance imaging (MRI), positron emission tomography (PET), cerebrospinal fluid (CSF), and genetic modality single-nucleotide polymorphism (SNP)]. This study demonstrates the performance of each method using these modalities individually or integratively, and may be valuable to clinical tests in practice. Our experimental results suggest that for AD prediction, in general, (1) in terms of accuracy, PET is the best modality; (2) Even though the discriminant power of genetic SNP features is weak, adding this modality to other modalities does help improve the classification accuracy; (3) HGM-FS works best among the three feature selection methods; (4) Some of the selected features are shared by all the feature selection methods, which may have high correlation with the disease. Using all the modalities on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the best accuracies, described as (mean ± standard deviation)%, among the three methods are (76.2 ± 11.3)% for AD vs. MCI, (94.8 ± 7.3)% for AD vs. HC, (76.5 ± 11.1)% for MCI vs. HC, and (71.0 ± 8.4)% for AD vs. MCI vs. HC, respectively.
publisher	Frontiers Media S.A.
publishDate	2014
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4201101/
_version_	1613146114041577472

Integrative analysis of multi-dimensional imaging genomics data for Alzheimer's disease prediction

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