American Statistical Association
In many high-throughput applications 'omics features can jointly affect the outcome in a complex way. One example of it is microbiome data, where microbes are known to interact and compete for the same resources. Understanding these complex relationships may help further elucidate the association between the microbiome and human health. In this talk we propose to explore the associations between the outcome and pairwise relationships between features, e.g. bacterial abundances, using a modification of the stability selection algorithm. It combines subsampling with penalized regression to identify important feature pairs while minimizing the number of false positives.
|Date:||Wednesday, May 24, 2017|
|Time:||4:00 - 5:00 P.M.|
Memorial Sloan Kettering Cancer Center
Department of Epidemiology and Biostatistics
485 Lexington Avenue
(Between 46th & 47th Streets)
2nd Floor, Conference Room B
New York, New York
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