Guan Lab

Department of Computational Medicine & Bioinformatics
Thu, 10/06/2016 - 16:37 -- gyuanfan
TitleCOMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease.
Publication TypeJournal Article
Year of Publication2016
AuthorsZhu F, Panwar B, Dodge HH, Li H, Hampstead BM, Albin RL, Paulson HL, Guan Y
JournalSci Rep
Date Published2016 Oct 05

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.


Alternate JournalSci Rep
PubMed ID27703197