Bayesian Structure Learning in Multilayered Genomic Networks
多层基因组网络中的贝叶斯结构学习AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要直接研究多层网络结构、边选择及节点间依赖，对应网络方法方向，应用对象为基因组。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
多层基因组网络中的贝叶斯结构学习
- 研究问题
- 如何整合多个基因组平台的数据，刻画具有有序层次、层内与层间依赖的网络，并识别癌症相关系统中的条件独立结构？
- 方法与思路
- 作者提出多层高斯图模型及贝叶斯逐节点选择方法，通过变量选择和边特异的先验知识，估计稀疏且可解释的网络结构。
- 材料陈述的贡献
- 摘要称所提方法在模拟中优于若干多变量回归方法，并通过跨癌症类型的信号通路分析展示网络共性、差异及潜在生物学用途。
- 阅读关注点
- 应核对有向与无向关系的模型表达、逐节点选择的一致处理方式、边先验的来源，以及模拟比较和生物学解释的证据。
- 阅读边界
- 仅据版本未注明的索引摘要；条件依赖不能自动解释为因果关系，生物标志物和治疗靶点用途仍属潜在应用。
原文摘要
Integrative network modeling of data arising from multiple genomic platforms provides insight into the holistic picture of the interactive system, as well as the flow of information across many disease domains including cancer. The basic data structure consists of a sequence of hierarchically ordered datasets for each individual subject, which facilitates integration of diverse inputs, such as genomic, transcriptomic, and proteomic data. A primary analytical task in such contexts is to model the layered architecture of networks where the vertices can be naturally partitioned into ordered layers, dictated by multiple platforms, and exhibit both undirected and directed relationships. We propose a multi-layered Gaussian graphical model (mlGGM) to investigate conditional independence structures in such multi-level genomic networks in human cancers. We implement a Bayesian node-wise selection (BANS) approach based on variable selection techniques that coherently accounts for the multiple types of dependencies in mlGGM; this flexible strategy exploits edge-specific prior knowledge and selects sparse and interpretable models. Through simulated data generated under various scenarios, we demonstrate that BANS outperforms other existing multivariate regression-based methodologies. Our integrative genomic network analysis for key signaling pathways across multiple cancer types highlights commonalities and differences of p53 integrative networks and epigenetic effects of BRCA2 on p53 and its interaction with T68 phosphorylated CHK2, that may have translational utilities of finding biomarkers and therapeutic targets.