Learning Hyperbolic Embedding for Phylogenetic Tree Placement and Updates.

Area of Science:

• Computational Biology
• Bioinformatics
• Machine Learning

Background:

• Phylogenetic placement is crucial for ecological analyses, involving adding new species to existing phylogenetic trees.
• A prior deep learning method estimated distances between species using gene sequences mapped to high-dimensional Euclidean space.

Purpose of the Study:

• To investigate the suitability of hyperbolic geometry for embedding gene sequences and preserving species tree distances.
• To address the computational challenges associated with hyperbolic embeddings.

Main Methods:

• Examined hyperbolic spaces as an alternative to Euclidean space for representing distances between gene sequences.
• Developed methods to overcome unique challenges of hyperbolic arithmetic, exponential functions, and limited precision.
• Applied a deep learning framework for phylogenetic placement and tree updating.

Main Results:

• Hyperbolic embeddings demonstrated significantly lower distance errors compared to Euclidean embeddings.
• Improved distance accuracy did not consistently translate to enhanced phylogenetic placement accuracy.
• The deep learning framework, utilizing hyperbolic embeddings, accurately updated species trees with minimal genetic data.

Conclusions:

• Hyperbolic geometry offers a more accurate representation of distances for gene sequence embeddings in phylogenetic analyses.
• While beneficial, hyperbolic embeddings require careful implementation to maximize their utility in phylogenetic placement.
• The proposed deep learning framework effectively leverages hyperbolic geometry for accurate species tree updating.