Inferring phylogenetic networks from multifurcating trees via cherry picking and machine learning

Area of Science:

• Computational Biology
• Phylogenetics
• Machine Learning

Background:

• The Hybridization problem aims to create a single phylogenetic network from conflicting trees, minimizing reticulation nodes.
• Existing solutions struggle with large or complex datasets, particularly multifurcating trees with differing taxon sets.

Purpose of the Study:

• To present FHyNCH, the first algorithmic framework for heuristically solving the Hybridization problem on large, diverse phylogenetic tree datasets.
• To extend previous work on binary trees to handle multifurcating trees with non-identical taxon sets.

Main Methods:

• FHyNCH combines the cherry-picking technique with two novel machine-learning models.
• The framework is designed to handle large sets of multifurcating trees with potentially different taxon sets.

Main Results:

• FHyNCH demonstrates practical applicability for solving the Hybridization problem.
• The methods produce qualitatively good solutions for phylogenetic network construction.

Conclusions:

• FHyNCH offers a significant advancement in computationally reconciling complex phylogenetic data.
• This framework enables more accurate and scalable phylogenetic network inference for diverse biological datasets.