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Zichao Yan

Alumni

Publications

PhyloGFN: Phylogenetic inference with generative flow networks
Ming Yang Zhou
Zichao Yan
Elliot Layne
Nikolay Malkin
Dinghuai Zhang
Moksh J. Jain
Mathieu Blanchette
Yoshua Bengio
2024-01-16
ICLR.cc/2024/Conference (poster)
doi.org
openreview.net
Towards equilibrium molecular conformation generation with GFlowNets
Alexandra Volokhova
Michał Koziarski
Alex Hernandez-Garcia
Cheng-Hao Liu
Santiago Miret
Pablo Lemos
Luca Thiede
Zichao Yan
Alan Aspuru-Guzik
Yoshua Bengio
Sampling diverse, thermodynamically feasible molecular conformations plays a crucial role in predicting properties of a molecule. In this pa… (see more)per we propose to use GFlowNet for sampling conformations of small molecules from the Boltzmann distribution, as determined by the molecule's energy. The proposed approach can be used in combination with energy estimation methods of different fidelity and discovers a diverse set of low-energy conformations for highly flexible drug-like molecules. We demonstrate that GFlowNet can reproduce molecular potential energy surfaces by sampling proportionally to the Boltzmann distribution.
2023-10-27
NeurIPS.cc/2023/Workshop/AI4Mat (poster)
doi.org
openreview.net
PhyloGFN: Phylogenetic inference with generative flow networks
Ming Yang Zhou
Zichao Yan
Elliot Layne
Nikolay Malkin
Dinghuai Zhang
Moksh J. Jain
Mathieu Blanchette
Yoshua Bengio
Phylogenetics is a branch of computational biology that studies the evolutionary relationships among biological entities. Its long history a… (see more)nd numerous applications notwithstanding, inference of phylogenetic trees from sequence data remains challenging: the high complexity of tree space poses a significant obstacle for the current combinatorial and probabilistic techniques. In this paper, we adopt the framework of generative flow networks (GFlowNets) to tackle two core problems in phylogenetics: parsimony-based and Bayesian phylogenetic inference. Because GFlowNets are well-suited for sampling complex combinatorial structures, they are a natural choice for exploring and sampling from the multimodal posterior distribution over tree topologies and evolutionary distances. We demonstrate that our amortized posterior sampler, PhyloGFN, produces diverse and high-quality evolutionary hypotheses on real benchmark datasets. PhyloGFN is competitive with prior works in marginal likelihood estimation and achieves a closer fit to the target distribution than state-of-the-art variational inference methods. Our code is available at https://github.com/zmy1116/phylogfn.
2023-10-12
ArXiv (preprint)
doi.org
arxiv.org
PhyloPGM: boosting regulatory function prediction accuracy using evolutionary information
Faizy Ahsan
Zichao Yan
Doina Precup
Mathieu Blanchette
Abstract Motivation The computational prediction of regulatory function associated with a genomic sequence is of utter importance in -omics … (see more)study, which facilitates our understanding of the underlying mechanisms underpinning the vast gene regulatory network. Prominent examples in this area include the binding prediction of transcription factors in DNA regulatory regions, and predicting RNA–protein interaction in the context of post-transcriptional gene expression. However, existing computational methods have suffered from high false-positive rates and have seldom used any evolutionary information, despite the vast amount of available orthologous data across multitudes of extant and ancestral genomes, which readily present an opportunity to improve the accuracy of existing computational methods. Results In this study, we present a novel probabilistic approach called PhyloPGM that leverages previously trained TFBS or RNA–RBP binding predictors by aggregating their predictions from various orthologous regions, in order to boost the overall prediction accuracy on human sequences. Throughout our experiments, PhyloPGM has shown significant improvement over baselines such as the sequence-based RNA–RBP binding predictor RNATracker and the sequence-based TFBS predictor that is known as FactorNet. PhyloPGM is simple in principle, easy to implement and yet, yields impressive results. Availability and implementation The PhyloPGM package is available at https://github.com/BlanchetteLab/PhyloPGM Supplementary information Supplementary data are available at Bioinformatics online.
2022-06-27
Bioinformatics (published)
doi.org