PhyloGFN: Phylogenetic Inference with Generative Flow Networks

Core Concepts

Generative Flow Networks (GFlowNets) offer a novel approach to phylogenetic inference, producing diverse and high-quality evolutionary hypotheses.

Abstract

The content introduces PhyloGFN, a GFlowNet-based method for phylogenetic inference. It discusses the challenges in phylogenetics, the framework of GFlowNets, and the specific adaptations for Bayesian and parsimony-based inference. The paper presents results comparing PhyloGFN to existing methods on real datasets, showcasing its competitive performance in marginal likelihood estimation and ability to model the entire tree topology space effectively. Abstract: Phylogenetics studies evolutionary relationships among biological entities. Challenges in inferring phylogenetic trees due to large tree space. Adoption of generative flow networks for Bayesian and parsimony-based inference. Demonstration of PhyloGFN's effectiveness on benchmark datasets. Introduction: Importance of accurate phylogenetic inference in computational biology. Challenges posed by complex tree spaces for maximum likelihood and maximum parsimony methods. Introduction of generative flow networks for improved sampling from posterior distributions. Data Extraction: "Published as a conference paper at ICLR 2024" "PhyloGFN is competitive with prior works in marginal likelihood estimation"

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Published as a conference paper at ICLR 2024 PhyloGFN is competitive with prior works in marginal likelihood estimation

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Key Insights Distilled From

PhyloGFN

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

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

Deeper Inquiries

How does PhyloGFN address the challenges posed by the large tree space in phylogenetics

PhyloGFN addresses the challenges posed by the large tree space in phylogenetics by adopting a framework of generative flow networks (GFlowNets). The extremely large tree space, with (2𝑛−5)!! unique unrooted bifurcating tree topologies for 𝑛 species, poses a significant obstacle for current combinatorial and probabilistic techniques. PhyloGFN tackles this challenge by leveraging GFlowNets, which are well-suited for sampling complex combinatorial structures. These networks are able to explore and sample from the multimodal posterior distribution over tree topologies and evolutionary distances efficiently. By using an acyclic Markov decision process with fully customizable reward functions, PhyloGFN is trained to construct phylogenetic trees in a bottom-up fashion. This approach allows PhyloGFN to navigate through the vast tree space effectively and produce diverse and high-quality evolutionary hypotheses on real benchmark datasets.

What are the implications of using generative flow networks like PhyloGFN for future advancements in computational biology

The implications of using generative flow networks like PhyloGFN for future advancements in computational biology are significant. These models offer a promising solution to challenging problems in phylogenetics, such as Bayesian inference and parsimony-based analysis. By leveraging GFlowNets' ability to sample from complex distributions over structured spaces, researchers can improve the accuracy and efficiency of phylogenetic inference tasks. Additionally, the adaptability of PhyloGFN to model continuous branch lengths opens up new possibilities for more accurate modeling of evolutionary processes. In computational biology, where understanding genetic relationships among species is crucial for various applications like drug development or disease research, advanced methods like PhyloGFN can lead to breakthroughs in analyzing biological data.

How can the adaptability of PhyloGFN to continuous branch length modeling impact its performance in Bayesian inference

The adaptability of PhyloGFN to continuous branch length modeling can have a significant impact on its performance in Bayesian inference tasks. By incorporating continuous variables that capture the level of sequence divergence along each branch of the tree, PhyloGFN enhances its capacity to model complex evolutionary processes more accurately. This adaptation allows PhyloGFN to better capture subtle variations in sequence data and improve its ability to estimate posterior probabilities over different tree topologies effectively. As a result, this flexibility enables PhyloGFN to achieve state-of-the-art performance in Bayesian phylogenetic inference by providing more precise estimates of marginal likelihoods and closer fits to target distributions compared to traditional methods that rely on discrete representations only.