Tropical Data Science

TL;DR

This paper surveys recent machine learning methods leveraging tropical geometry to analyze phylogenetic tree data, addressing challenges posed by the non-Euclidean structure of tree spaces.

Contribution

It introduces the application of tropical geometry to machine learning models for phylogenetic trees, highlighting new approaches to analyze complex tree data.

Findings

Tropical geometry enables new analysis tools for phylogenetic trees.

Machine learning models adapted to tropical geometry improve data analysis.

The survey discusses recent advances and future directions in the field.

Abstract

Phylogenomics is a new field which applies to tools in phylogenetics to genome data. Due to a new technology and increasing amount of data, we face new challenges to analyze them over a space of phylogenetic trees. Because a space of phylogenetic trees with a fixed set of labels on leaves is not Euclidean, we cannot simply apply tools in data science. In this paper we survey some new developments of machine learning models using tropical geometry to analyze a set of phylogenetic trees over a tree space.