Computational biology models are often missing information, such as the concentration of biochemical species of interest. One approach to dealing with this missing information is to place a probabilistic prior over the missing data. One possible choice for such a prior is a Gaussian process.
In this tutorial we will give an introduction to Gaussian processes. We will give simple examples of Gaussian processes in regression and interpolation. We will then show how Gaussian processes can be incorporated with differential equation models to give probabilistic models for transcription. Such models can then be used to rank potential targets of given transcription factors.