DynGFN：利用GFlowNets实现基因调控网络的贝叶斯推断
DynGFN: Towards Bayesian Inference of Gene Regulatory Networks with GFlowNets
AI总结：
DynGFN利用RNA速度信息和GFlowNets，在贝叶斯框架下同时处理基因调控网络的环状结构和不确定性，学习更优的后验分布。
摘要：
细胞生物学的一大挑战是推断基因调控网络（GRN），该网络描述了控制基因表达和细胞功能的基因及其产物之间的相互作用。我们可以将此视为因果发现问题，但面临两个非标准挑战：（1）调控网络本质上是环状的，因此我们不应将GRN建模为有向无环图（DAG）；（2）观测数据具有显著的测量噪声，因此对于典型的样本量，总会有大量在给定数据下可能成立的图等价类，我们需要能够捕捉这种不确定性的方法。现有方法要么专注于挑战（1），从动力学中识别环状结构，要么专注于挑战（2），学习DAG上的复杂贝叶斯后验，但无法同时解决两者。在本文中，我们利用RNA速度技术可以估计基因表达“速度”的事实，开发了一种同时应对这两个挑战的方法。由于我们可以获得速度信息，我们可以将贝叶斯结构学习问题视为动力系统的稀疏识别问题，通过时间捕捉环状反馈回路。由于我们的目标是对离散结构的不确定性进行建模，我们利用生成流网络（GFlowNets）来估计可能的稀疏依赖组合空间上的后验分布。我们的结果表明，与最先进的贝叶斯结构学习方法相比，我们的方法学习到的后验能更好地包含环状结构的分布。
英文摘要：
One of the grand challenges of cell biology is inferring the gene regulatory network (GRN) which describes interactions between genes and their products that control gene expression and cellular function. We can treat this as a causal discovery problem but with two non-standard challenges: (1) regulatory networks are inherently cyclic so we should not model a GRN as a directed acyclic graph (DAG), and (2) observations have significant measurement noise, so for typical sample sizes there will always be a large equivalence class of graphs that are likely given the data, and we want methods that capture this uncertainty. Existing methods either focus on challenge (1), identifying cyclic structure from dynamics, or on challenge (2) learning complex Bayesian posteriors over DAGs, but not both. In this paper we leverage the fact that it is possible to estimate the "velocity" of gene expression with RNA velocity techniques to develop an approach that addresses both challenges. Because we have access to velocity information, we can treat the Bayesian structure learning problem as a problem of sparse identification of a dynamical system, capturing cyclic feedback loops through time. Since our objective is to model uncertainty over discrete structures, we leverage Generative Flow Networks (GFlowNets) to estimate the posterior distribution over the combinatorial space of possible sparse dependencies. Our results indicate that our method learns posteriors that better encapsulate the distributions of cyclic structures compared to counterpart state-of-the-art Bayesian structure learning approaches.
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