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25.04.2018 | Original Article

GPU Accelerated Browser for Neuroimaging Genomics

verfasst von: Bob Zigon, Huang Li, Xiaohui Yao, Shiaofen Fang, Mohammad Al Hasan, Jingwen Yan, Jason H. Moore, Andrew J. Saykin, Li Shen, Alzheimer’s Disease Neuroimaging Initiative

Erschienen in: Neuroinformatics | Ausgabe 3-4/2018

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Abstract

Neuroimaging genomics is an emerging field that provides exciting opportunities to understand the genetic basis of brain structure and function. The unprecedented scale and complexity of the imaging and genomics data, however, have presented critical computational bottlenecks. In this work we present our initial efforts towards building an interactive visual exploratory system for mining big data in neuroimaging genomics. A GPU accelerated browsing tool for neuroimaging genomics is created that implements the ANOVA algorithm for single nucleotide polymorphism (SNP) based analysis and the VEGAS algorithm for gene-based analysis, and executes them at interactive rates. The ANOVA algorithm is 110 times faster than the 4-core OpenMP version, while the VEGAS algorithm is 375 times faster than its 4-core OpenMP counter part. This approach lays a solid foundation for researchers to address the challenges of mining large-scale imaging genomics datasets via interactive visual exploration.

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Titel: GPU Accelerated Browser for Neuroimaging Genomics
verfasst von: Bob Zigon
Huang Li
Xiaohui Yao
Shiaofen Fang
Mohammad Al Hasan
Jingwen Yan
Jason H. Moore
Andrew J. Saykin
Li Shen
Alzheimer’s Disease Neuroimaging Initiative
Publikationsdatum: 25.04.2018
Verlag: Springer US
Erschienen in: Neuroinformatics / Ausgabe 3-4/2018
Print ISSN: 1539-2791
Elektronische ISSN: 1559-0089
DOI: https://doi.org/10.1007/s12021-018-9376-y

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