A Machine Learning Approach to Resolving Incongruence in Molecular Phylogenies and Visualization Analysis

Abstract

The incongruence between gene trees and species trees is one of the most pervasive challenges in molecular phylogenetics. In this work, a machine learning approach is proposed to overcome this problem. In the machine learning approach, the gene data set is clustered by a self-organizing map (SOM). Then a phylogenetically informative core gene set is created by combining the maximum entropy gene from each cluster to conduct phylogenetic analysis. Using the same data set, this approach performs better than the previous random gene concatenation method. The SOM based information visualization is also employed to compare the species patterns in the phylogenetic tree constructions.