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Abstract Details
Activity Number:
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470
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #304701 |
Title:
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Identifying Potentially Immunologically Relevant Regions in the HIV Envelope Protein fia Logistic Kernel Machine Regression Models
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Author(s):
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Saheli Datta*+ and Youyi Fong and Georgia D. Tomaras
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Companies:
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Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center and Duke Human Vaccine Institute
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Address:
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2849 Eastlake Ave E, Seattle, WA, 98102, United States
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Keywords:
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Abstract:
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A case-control analysis conducted to identify immune correlates of infection risk following the RV144 HIV-1 vaccine trial found that plasma IgA HIV-1 Env binding antibodies were directly correlated with HIV-1 infection rate. In order to better understand the mechanism through which IgA binding correlates with infection status, it is worthwhile to identify regions of the gp120 protein sequence where IgA binding correlates with infection risk.
We use a logistic kernel machine regression method (Liu, Ghosh and Lin 2008) to model the effect of the protein sequence. This method models the effect of protein sequences as a random effect, whose covariance matrix depends on the pairwise distance matrix between the protein sequences. We consider a match-mismatch metric, a physio-chemical properties-based metric, and a profile hidden Markov model based-metric. A score test is used to test for the effect of the amino acid sequences on the outcome.
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