Estimation of Directed Acyclic Graphs Through Two-Stage Adaptive Lasso for Gene Network Inference
用于基因网络推断的两阶段自适应 Lasso 有向无环图估计AI 解读 ↓
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用于基因网络推断的两阶段自适应 Lasso 有向无环图估计
- 研究问题
- 在不知道节点顺序的高维基因表达数据中，如何应对无环约束和观测等价类问题，以估计有向无环图结构？
- 方法与思路
- 作者提出 NS-DIST 两阶段自适应 Lasso：先进行邻域选择，再在筛选后的邻域中使用带禁忌机制的离散改进搜索。
- 材料陈述的贡献
- 摘要称通过模拟展示方法的有效性和计算效率，并用两个真实数据案例说明其在基因调控网络推断中的用途。
- 阅读关注点
- 应核对邻域筛选能否保留真实边、搜索如何满足无环约束，以及观测等价类如何处理和边方向可识别的条件。
- 阅读边界
- OpenAlex 摘要未注明稿件版本；虽以因果关系为动机，但未说明如何识别唯一方向，不能将估计图直接视为已识别因果图。
原文摘要
Graphical models are a popular approach to find dependence and conditional independence relationships between gene expressions. Directed acyclic graphs (DAGs) are a special class of directed graphical models, where all the edges are directed edges and contain no directed cycles. The DAGs are well known models for discovering causal relationships between genes in gene regulatory networks. However, estimating DAGs without assuming known ordering is challenging due to high dimensionality, the acyclic constraints, and the presence of equivalence class from observational data. To overcome these challenges, we propose a two-stage adaptive Lasso approach, called NS-DIST, which performs neighborhood selection (NS) in stage 1, and then estimates DAGs by the Discrete Improving Search with Tabu (DIST) algorithm within the selected neighborhood. Simulation studies are presented to demonstrate the effectiveness of the method and its computational efficiency. Two real data examples are used to demonstrate the practical usage of our method for gene regulatory network inference.