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Combining heterogeneous genomics data to understand complex human traits. Mostavafi, Sara
Description
Recent availability of large-scale multi-omics datasets from human cohort studies present new opportunities for deriving molecular mechanisms for complex disease. However, a central challenge in using these genomics data to understand complex traits is to disentangle causal, and hence reproducible and meaningful associations, from merely correlated ones. In this talk, Iâ ll describe ongoing projects that develop new statistical and computational methods for integrative analysis of multi-omics data, with the ultimate goal of providing insights into complex human diseases including major depression.
Item Metadata
Title |
Combining heterogeneous genomics data to understand complex human traits.
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Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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Date Issued |
2018-11-08T15:34
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Description |
Recent availability of large-scale multi-omics datasets from human cohort studies present new opportunities for deriving molecular mechanisms for complex disease. However, a central challenge in using these genomics data to understand complex traits is to disentangle causal, and hence reproducible and meaningful associations, from merely correlated ones. In this talk, Iâ ll describe ongoing projects that develop new statistical and computational methods for integrative analysis of multi-omics data, with the ultimate goal of providing insights into complex human diseases including major depression.
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Extent |
24.0
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Subject | |
Type | |
File Format |
video/mp4
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Language |
eng
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Notes |
Author affiliation: University of British Columbia
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Series | |
Date Available |
2019-05-08
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0378635
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URI | |
Affiliation | |
Peer Review Status |
Unreviewed
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Scholarly Level |
Researcher
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Rights URI | |
Aggregated Source Repository |
DSpace
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International