# SCH

## Seminar

### High dimensional inference in bioinformatics and genomics

Seminar Room 1, Newton Institute

#### Abstract

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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