Using Neural Nets to Estimate Evolutionary Parameters

- 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 paper we explore the feasibility of using machine learning algorithms, in particular 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.

• Type: Conference
• Year: 2006
• Where: BIOCOMP
• Authors: Chi-Chiang Lee, Paul Marjoram
• Keywords: BIOCOMP 2006, BIOCOMP 2007, Machine Learning Algorithms, Molecular Genetic Data, Particular Neural Networks