<p>Uncovering Alzheimer&apos;s SNP signature with a multi-view machine learning analysis based on SNPs, genes and pathways</p>

Margherita Squillario; Matteo Barbieri; Samuele Fiorini; Federico Tomasi; Annalisa Barla

doi:10.7490/f1000research.1116765.1

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Squillario M, Barbieri M, Fiorini S et al. Uncovering Alzheimer's SNP signature with a multi-view machine learning analysis based on SNPs, genes and pathways [version 1; not peer reviewed]. F1000Research 2019, 8:685 (poster) (https://doi.org/10.7490/f1000research.1116765.1)

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Uncovering Alzheimer's SNP signature with a multi-view machine learning analysis based on SNPs, genes and pathways

Margherita Squillario¹, Matteo Barbieri, Samuele Fiorini, Federico Tomasi, Annalisa Barla

Published 20 May 2019 (https://doi.org/10.7490/f1000research.1116765.1)

Author Affiliations

¹ DIBRIS - University of Genoa, Italy

Copyright © 2019 Squillario M et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 20 May 2019

Uncovering Alzheimer's SNP signature with a multi-view machine learning analysis based on SNPs, genes and pathways
[version 1; not peer reviewed]

Margherita Squillario¹, Matteo Barbieri, Samuele Fiorini, Federico Tomasi, Annalisa Barla

Author Affiliations

¹ DIBRIS - University of Genoa, Italy

Presented at

13th International Conference on Alzheimer’s and Parkinson’s Diseases and related neurological disorders (AD/PD) 2017

Abstract

Competing Interests

No competing interests were disclosed

Keywords

Late onset Alzheimer’s Disease, GWAS, 3-fold kernel approach, machine learning, SNPs and genes and pathways signatures, new representation of SNP data, signature validation.

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