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Phone: (843) 588-2001
Edgar Allan Poe/Sullivan's Island Library
Closed for renovations
Phone: (843) 883-3914
West Ashley Library
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Wando Mount Pleasant Library
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Village Library
9 a.m. - 6 p.m.
Phone: (843) 884-9741
St. Paul's/Hollywood Library
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McClellanville Library
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Keith Summey North Charleston Library
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Hurd/St. Andrews Library
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Heterogeneous multimodal biomarkers analysis for Alzheimer's disease via Bayesian network.
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- Author(s): Jin, Yan; Su, Yi; Zhou, Xiao-Hua; Huang, Shuai
- Source:
EURASIP Journal on Bioinformatics & Systems Biology; 8/19/2016, Vol. 2016 Issue 1, p1-8, 8p- Subject Terms:
- Source:
- Additional Information
- Abstract: By 2050, it is estimated that the number of worldwide Alzheimer's disease (AD) patients will quadruple from the current number of 36 million, while no proven disease-modifying treatments are available. At present, the underlying disease mechanisms remain under investigation, and recent studies suggest that the disease involves multiple etiological pathways. To better understand the disease and develop treatment strategies, a number of ongoing studies including the Alzheimer's Disease Neuroimaging Initiative (ADNI) enroll many study participants and acquire a large number of biomarkers from various modalities including demographic, genotyping, fluid biomarkers, neuroimaging, neuropsychometric test, and clinical assessments. However, a systematic approach that can integrate all the collected data is lacking. The overarching goal of our study is to use machine learning techniques to understand the relationships among different biomarkers and to establish a system-level model that can better describe the interactions among biomarkers and provide superior diagnostic and prognostic information. In this pilot study, we use Bayesian network (BN) to analyze multimodal data from ADNI, including demographics, volumetric MRI, PET, genotypes, and neuropsychometric measurements and demonstrate our approach to have superior prediction accuracy. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of EURASIP Journal on Bioinformatics & Systems Biology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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