Inferring gene-gene associations and gene networks beyond standard statistical models

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• Haiyan Huang (University of California, Berkeley)
• Tuesday 12 July 2016, 13:30-14:00
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact INI IT.

SNAW01 - Graph limits and statistics

With the advent of high-throughput technologies making large-scale gene expression data readily available, developing appropriate computational tools to infer gene interactions has been a major challenge in systems biology. In this talk, I will discuss two methods of finding gene associations that differ in their considerations of how genes behave across the given samples. The first method applies to the case where the patterns of gene association may change or only exist in a subset of all the samples. The second method goes beyond pairwise gene relationships to higher level group interactions, but requiring similar gene behaviours across all the samples. We compare both methods to other popular approaches using simulated and real data, and demonstrate they lead to better general performance and capture important biological features in certain situations that are missed by the other methods.

This talk is part of the Isaac Newton Institute Seminar Series series.

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