Keskeiset käsitteet
Generative Flow Networks (GFlowNets) offer a novel approach to phylogenetic inference, producing diverse and high-quality evolutionary hypotheses.
Tiivistelmä
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"
Tilastot
Published as a conference paper at ICLR 2024
PhyloGFN is competitive with prior works in marginal likelihood estimation