DeepED: A Deep Learning Framework for Estimating Evolutionary Distances

Abstract

Evolutionary distances refer to the number of substitutions per site in two aligned nucleotide or amino acid sequences, which reflect divergence time and are much significant for phylogenetic inferences. In the past several decades, lots of molecular evolution models have been proposed for evolutionary distance estimation. Most of these models are designed under more or less assumptions and some assumptions are in good agreement with some real-world data but not all. To relax these assumptions and improve accuracies in evolutionary distance estimation, this paper proposes a framework containing Deep Neural Networks (DNNs), called DeepED (Deep learning method to estimate Evolutionary Distances), to estimate evolutionary distances for aligned DNA sequence pairs. The purposely designed structure in this framework enables it to handle long and variable length sequences as well as to find important segments in a sequence. The models of the network are trained with reliable data from real world which includes highly credible phylogenetic inferences. Experimental results demonstrate that DeepED models achieve a accuracy up to 0.98 (R-Squared), which outperforms traditional methods.