PhyloGFN: Phylogenetic Inference with Generative Flow Networks

Основні поняття

Generative Flow Networks (GFlowNets) offer a novel approach to phylogenetic inference, providing competitive results in Bayesian and parsimony-based analyses.

Анотація

The content introduces PhyloGFN, a novel approach to phylogenetic inference using GFlowNets. It addresses challenges in phylogenetics by sampling from complex combinatorial structures. The paper discusses the framework, training objectives, model architecture, and performance evaluation on real datasets. Results show that PhyloGFN outperforms existing methods in marginal likelihood estimation and provides a closer fit to the target distribution.
Directory:

Introduction to PhyloGFN

Authors and Affiliations
Abstract Overview

Background on Phylogenetic Inference

Challenges in Phylogenetics
Prior Work on MCMC and VI Approaches

PhyloGFN for Bayesian Inference

Model Architecture for Bayesian Analysis
Reward Function and State Representation

Marginal Log-Likelihood Estimation

Comparison with Baseline Methods (MrBayes, VBPI-GNN, etc.)

Parsimony-Based Phylogenetic Inference

Comparison with PAUP*

Experiments and Results

Evaluation on Real Datasets

Discussion and Future Work

Статистика

"PhyloGFN is competitive with prior works in marginal likelihood estimation."
"PhyloGFN achieves a closer fit to the target distribution than state-of-the-art variational inference methods."

Цитати

Ключові висновки, отримані з

PhyloGFN

by Mingyang Zho... о arxiv.org 03-26-2024

https://arxiv.org/pdf/2310.08774.pdf

Глибші Запити

How does the use of GFlowNets impact the scalability of phylogenetic inference methods

The use of GFlowNets has a significant impact on the scalability of phylogenetic inference methods. Traditional methods for phylogenetic inference, such as MCMC-based algorithms and variational inference approaches, often struggle with scalability when dealing with large datasets or complex tree spaces. The framework of generative flow networks (GFlowNets) offers a promising solution to this challenge. By treating the generation of phylogenetic trees as a sequential decision-making problem on an acyclic deterministic Markov Decision Process (MDP), GFlowNets provide a structured approach that can efficiently sample from complex combinatorial structures like the vast space of tree topologies.
One key advantage is that GFlowNets are well-suited for sampling discrete objects from multimodal distributions, making them ideal for exploring and sampling from the multimodal posterior distribution over tree topologies and evolutionary distances in phylogenetics. This capability allows PhyloGFN to navigate through the extremely large tree space efficiently, overcoming the scalability limitations faced by traditional methods.

What are the implications of PhyloGFN's ability to sample from complex combinatorial structures

The ability of PhyloGFN to sample from complex combinatorial structures has several implications for phylogenetic inference:

Exploration in Vast Tree Space: PhyloGFN's capacity to explore and sample from the entire phylogenetic tree space enables it to consider a wide range of possible evolutionary hypotheses. This leads to more diverse and high-quality solutions compared to traditional methods.

High-Fidelity Modeling: By leveraging generative flow networks, PhyloGFN can model modes within the posterior distribution over tree topologies effectively. This means that it can capture subtle variations in evolutionary relationships between biological entities.

Optimal Suboptimal Trees Estimation: PhyloGFN outperforms existing methods in estimating suboptimal trees' posterior probabilities accurately, providing insights into alternative evolutionary scenarios beyond just optimal solutions.

Efficient Sampling: The bottom-up construction procedure employed by PhyloGFN allows for efficient sampling without compromising accuracy or quality in generating phylogenetic trees.

Overall, these implications highlight how incorporating GFlowNet-based modeling enhances exploration capabilities and improves overall performance in inferring evolutionary relationships among biological entities.

How might incorporating continuous branch length modeling further enhance the performance of PhyloGFN

Incorporating continuous branch length modeling into PhyloGFN could further enhance its performance by addressing some limitations associated with discretizing branch lengths:

Improved Precision: Continuous branch length modeling allows for more precise representation of sequence divergence along each branch of the tree compared to discrete binning approaches used initially.

Enhanced Flexibility: Continuous models offer greater flexibility in capturing subtle variations in sequence evolution patterns across different branches.

Better Fit to Data Distribution: Modeling branch lengths continuously may lead to better alignment with actual data distributions observed in biological sequences.

4Reduced Information Loss: Discretization may result in information loss due to binning effects; continuous modeling helps mitigate this issue by preserving finer details present within data samples.
By incorporating continuous branch length modeling into PhyloGFNs training process would likely improve its accuracy precision while also enhancing its ability capture subtleties inherent within biological sequences during inferential tasks related reconstructing ancestral relationships among species based genetic information available at hand