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Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.

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Related Experiment Video

Updated: Dec 3, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Tumor Phylogeny Topology Inference via Deep Learning.

Erfan Sadeqi Azer¹, Mohammad Haghir Ebrahimabadi^1,2, Salem Malikić^1,2

¹Department of Computer Science, Indiana University, Bloomington, IN 47408, USA.

|October 29, 2020

Summary

This study introduces fast deep learning and reinforcement learning methods for tumor phylogeny reconstruction from single-cell sequencing data. These data-driven approaches efficiently infer tumor evolution, including tree topology and feasibility of perfect phylogeny.

Keywords:

Bioinformatics Cancer Phylogenetics

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Published on: August 16, 2020

Area of Science:

Computational biology
Genomics
Machine learning

Background:

Tumor phylogeny reconstruction from single-cell sequencing is crucial for understanding cancer evolution.
Existing computational methods struggle with large datasets due to the NP-hard nature of the perfect phylogeny problem.

Purpose of the Study:

To develop fast, data-driven computational approaches for tumor phylogeny reconstruction.
To infer key topological features and assess the feasibility of perfect phylogeny from genotype matrices.

Main Methods:

Deep learning for inferring linear vs. branching tree topologies.
Reinforcement learning for reconstructing the most likely tumor phylogeny.
Application to genotype matrices derived from single-cell sequencing.

Main Results:

Demonstrated fast deep learning solutions for topology inference.
Presented a reinforcement learning approach for phylogeny reconstruction.
Showcased the potential of data-driven methods in analyzing tumor evolution.

Conclusions:

Deep learning and reinforcement learning offer efficient alternatives for tumor phylogeny reconstruction.
These methods can accurately infer critical features of tumor evolutionary trajectories.
Data-driven approaches show promise for advancing cancer genomics research.