Genetic Epidemiology
Gene Expression Analysis
Statistical Genetics: other
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Gene Expression Analysis
Gene expression data derived from microarrays provide a promising tool for the diagnosis of molecular cancers. However, due to the large dimensions and the complexity of such data, it is challenging to find a reduced set of "informative genes" before a formal classification analysis. In the past few years, many marginal single-gene statistical measures have been applied to expression data despite the fact that gene-gene interactions are non-negligible. We are currently explore statistics that capture the interactions among genes. Via such statistics, one can reduce the dimension while retain more information and thus lead to a better classification performance. Discriminant analysis is different from gene mapping in general. We have also been working on algorithms that can use features identified through our new statistics for a better separation of classes. |
