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Qichen Huang

qichen.huang@mila.quebec
Maîtrise recherche - UdeM
Superviseur⋅e principal⋅e
Paul François
Sujets de recherche
Apprentissage par renforcement
Biologie computationnelle
Biophysique
Modèles génératifs
Systèmes dynamiques
Site web
GitHub

Publications

Sequence Generation and Phylogenetic Inference with Generative Flow Networks
Qichen Huang
Carlos Michel Mourra-Díaz
Xiaozhen Wen
D. Payette
A bstract Phylogenetic inference remains computationally challenging due to the exponentially growing tree topology search space, and curren… (voir plus)t methods rely heavily on multiple sequence alignments (MSAs) which are expensive and error-prone. We propose AncestorGFN, a proof-of-concept approach leveraging Generative Flow Networks (GFlowNets) for simultaneous sequence generation and phylogenetic exploration without requiring explicit MSAs. Our method learns to generate sequences matching a target distribution while the flow trajectories implicitly encode structural relationships among sequences. We demonstrate that greedy traceback on maximum-flow trajectories recovers shared intermediate states suggestive of common ancestry, and evaluate on the let-7 microRNA family where the learned flow structure qualitatively captures phylogenetic branching patterns. Furthermore, beam search at inference time discovers novel sequences clustering near known targets, suggesting applications in de novo sequence design. This work establishes an initial foundation for alignment-free phylogenetic exploration using generative models.
2026-04-08
bioRxiv (accepté)
doi.org