Publication Date

Spring 2021

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)

Department

Computer Science

First Advisor

Leonard Wesley

Second Advisor

Yulia Newton

Third Advisor

William Andreopoulous

Keywords

Predicting Alzheimer's, SVM, SNP Name, Allele1 - Plus, Allele2 - Plus, Chromosome, SNP

Abstract

A growing amount of neurodegenerative R&D is focused on identifying genomic- based explanations of AD that are beyond Amyloid-b and Tau. The proposed effort involves identifying some of the genomic variations, such as single nucleotide polymorphisms (SNPs), allele , chromosome, epigenetic contributors to MCI and AD that are beyond Aβ and Tau.

The project involves building a prediction model based on a support vector machine (SVM) classifier that takes into account the genomic variations and epigenetic factors to predict the early stage of mild cognitive impairment (MCI) and Alzheimer disease (AD). To achieve this, picking up important feature sets which will be input to the machine learning model were identified using statistical model tests. The data used in this research were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database / ADNI GO2 GWAS.

Future work may involve increase in sample size analyzed from ADNI DB, explore and analyze potential secondary effects/medical-conditions such as other diseases that might have influenced the observed results and separate out MCI from AD and further explore predictions and results.

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