High dimensional inference in bioinformatics and genomics
Seminar Room 1, Newton Institute
Bioinformatics came to the scene when biology started to automate its experiments. Although this would have led to large n and small p situations in other sciences, the complex nature of biology meant that it soon started to focus on lots of different variables, resulting in now well-known small n, large p situations. One such case is the inference of regulatory networks: the amount of networks is exponential in the number of nodes, whereas the available data is typically just a fraction thereof. We will present a penalized inference method that deals with such problems, that draws on experience with hypothesis testing. It has similarities with Approximate Bayesian Computation and seems to lead to exact inference in a few specific cases.
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