Booth Id:
CBIO047
Category:
Computational Biology and Bioinformatics
Year:
2017
Finalist Names:
Ravishanker, Pravin
Abstract:
Alzheimer’s affects 44 million people worldwide but has no preventive cures. This study created a novel methodology for Alzheimer’s (AD) and Mild Cognitive Impairment’s (MCI) early detection using data from the Alzheimer’s Disease Neuroimaging Initiative. Genome-Wide Association Analyses considered 14 million+ interactions between 608,586 SNP genetic variants and 23 disease endophenotypes linked to AD pathology; including Cerebrospinal Fluid protein levels, Florbetapir-PET (407 scans) beta-amyloid plaque levels, FDG-PET (427 scans) cerebral metabolic activity, and Resting-State fMRI (678 scans) functional network connectivity metrics computed using ICA, signal cross-correlation, and graph-rendering algorithms. The weighted additive effect of a subject’s multiple SNP variants, along with discovered SNP effect sizes on AD endophenotypes from association results, were utilized to compute 23 Polygenic Risk Scores per subject. With just a subject’s demographic info, cognitive scores, APOE-e4 (greatest genetic risk factor) allele dosage, and SNP Genotype Data for polygenic risk scoring, ALZCan’s gradient boosting ensemble machine learning engine differentiated between AD, MCI, and Healthy Controls with 98.10% diagnostic accuracy; and predicted onset of AD and MCI 12, 24, and 36 months into the future with 3-way prognostic accuracies of 91.72%, 85.38%, and 70.67%. By combining polygenic risk scores for risk prediction, intervention, and personalized medicine with machine learning for discovering patterns amongst high-dimensional genomic and neuroimaging datasets close to AD’s underlying etiology and progression, ALZCan revolutionizes Alzheimer’s screening, enabling prevention of further irreversible neurodegeneration and cognitive decline via early therapeutic intervention.
Awards Won: