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COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer’s disease
We present COMPASS, a COmputational Model to Predict the development of Alzheimer’s diSease Spectrum, to model Alzheimer’s disease (AD) progression. This was the best-performing method in recent crowdsourcing benchmark study, DREAM Alzheimer’s Disease Big Data challenge to predict changes in Mini-Me...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5050516/ https://www.ncbi.nlm.nih.gov/pubmed/27703197 http://dx.doi.org/10.1038/srep34567 |
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author | Zhu, Fan Panwar, Bharat Dodge, Hiroko H. Li, Hongdong Hampstead, Benjamin M. Albin, Roger L. Paulson, Henry L. Guan, Yuanfang |
author_facet | Zhu, Fan Panwar, Bharat Dodge, Hiroko H. Li, Hongdong Hampstead, Benjamin M. Albin, Roger L. Paulson, Henry L. Guan, Yuanfang |
author_sort | Zhu, Fan |
collection | PubMed |
description | We present COMPASS, a COmputational Model to Predict the development of Alzheimer’s diSease Spectrum, to model Alzheimer’s disease (AD) progression. This was the best-performing method in recent crowdsourcing benchmark study, DREAM Alzheimer’s Disease Big Data challenge to predict changes in Mini-Mental State Examination (MMSE) scores over 24-months using standardized data. In the present study, we conducted three additional analyses beyond the DREAM challenge question to improve the clinical contribution of our approach, including: (1) adding pre-validated baseline cognitive composite scores of ADNI-MEM and ADNI-EF, (2) identifying subjects with significant declines in MMSE scores, and (3) incorporating SNPs of top 10 genes connected to APOE identified from functional-relationship network. For (1) above, we significantly improved predictive accuracy, especially for the Mild Cognitive Impairment (MCI) group. For (2), we achieved an area under ROC of 0.814 in predicting significant MMSE decline: our model has 100% precision at 5% recall, and 91% accuracy at 10% recall. For (3), “genetic only” model has Pearson’s correlation of 0.15 to predict progression in the MCI group. Even though addition of this limited genetic model to COMPASS did not improve prediction of progression of MCI group, the predictive ability of SNP information extended beyond well-known APOE allele. |
format | Online Article Text |
id | pubmed-5050516 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-50505162016-10-11 COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer’s disease Zhu, Fan Panwar, Bharat Dodge, Hiroko H. Li, Hongdong Hampstead, Benjamin M. Albin, Roger L. Paulson, Henry L. Guan, Yuanfang Sci Rep Article We present COMPASS, a COmputational Model to Predict the development of Alzheimer’s diSease Spectrum, to model Alzheimer’s disease (AD) progression. This was the best-performing method in recent crowdsourcing benchmark study, DREAM Alzheimer’s Disease Big Data challenge to predict changes in Mini-Mental State Examination (MMSE) scores over 24-months using standardized data. In the present study, we conducted three additional analyses beyond the DREAM challenge question to improve the clinical contribution of our approach, including: (1) adding pre-validated baseline cognitive composite scores of ADNI-MEM and ADNI-EF, (2) identifying subjects with significant declines in MMSE scores, and (3) incorporating SNPs of top 10 genes connected to APOE identified from functional-relationship network. For (1) above, we significantly improved predictive accuracy, especially for the Mild Cognitive Impairment (MCI) group. For (2), we achieved an area under ROC of 0.814 in predicting significant MMSE decline: our model has 100% precision at 5% recall, and 91% accuracy at 10% recall. For (3), “genetic only” model has Pearson’s correlation of 0.15 to predict progression in the MCI group. Even though addition of this limited genetic model to COMPASS did not improve prediction of progression of MCI group, the predictive ability of SNP information extended beyond well-known APOE allele. Nature Publishing Group 2016-10-05 /pmc/articles/PMC5050516/ /pubmed/27703197 http://dx.doi.org/10.1038/srep34567 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Article Zhu, Fan Panwar, Bharat Dodge, Hiroko H. Li, Hongdong Hampstead, Benjamin M. Albin, Roger L. Paulson, Henry L. Guan, Yuanfang COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer’s disease |
title | COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer’s disease |
title_full | COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer’s disease |
title_fullStr | COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer’s disease |
title_full_unstemmed | COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer’s disease |
title_short | COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer’s disease |
title_sort | compass: a computational model to predict changes in mmse scores 24-months after initial assessment of alzheimer’s disease |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5050516/ https://www.ncbi.nlm.nih.gov/pubmed/27703197 http://dx.doi.org/10.1038/srep34567 |
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