Using artificial neural networks to estimate evolutionary parameters.

摘要

The rapid growth in the amount of molecular genetic data being collected will, in many cases, require the development of new analytic methods for the analysis of that data. In this thesis, we explore the feasibility of using machine learning algorithms, in particular artificial neural networks, to estimate two evolutionary parameters of great interest: mutation and recombination rates. We show that this is possible, and that the performance of such methods depends crucially upon the existence of good summary statistics appropriate for the given parameter, as well as the format in which the data itself is represented.