DAGBagM: learning directed acyclic graphs of mixed variables with an application to identify protein biomarkers for treatment response in ovarian cancer.
DAGBagM: learning directed acyclic graphs of mixed variables with an application to identify protein biomarkers for treatment response in ovarian cancer.
复制标题
DOI：
10.1186/s12859-022-04864-y
复制
发表时间：
2022-08-05
影响因子：
3
通讯作者：
中科院分区：
文献类型：
--
作者：
将有向无环图（DAG）模型应用于蛋白质基因组数据已被证明可有效检测复杂疾病的因果生物标志物。然而，在DAG学习中仍然存在未解决的挑战，以联合建模二进制临床结果变量和连续生物标志物测量。在本文中，我们提出了一个新的工具，DAGBagM，学习DAG与连续和二进制节点。通过使用适当的模型，DAGBagM允许连续或二进制节点成为父节点或子节点。它采用了自举聚合策略，以减少边缘推理的误报。同时，聚合过程提供了一个灵活的框架，以鲁棒地将先验信息的边缘。通过大量的仿真实验，我们证明了DAGBagM具有上级性能相比，其他策略建模混合类型的节点。此外，DAGBagM是计算效率比两个竞争的方法。当DAGbagM应用于卵巢癌研究的蛋白基因组数据集时，我们确定了卵巢癌铂难治性/耐药反应的潜在蛋白质生物标志物。DAGBagM作为github存储库在https://github.com/jie108/dagbagM上提供。在线版本包含补充材料，可在10.1186/s12859-022-04864-y获得。
Applying directed acyclic graph (DAG) models to proteogenomic data has been shown effective for detecting causal biomarkers of complex diseases. However, there remain unsolved challenges in DAG learning to jointly model binary clinical outcome variables and continuous biomarker measurements. In this paper, we propose a new tool, DAGBagM, to learn DAGs with both continuous and binary nodes. By using appropriate models, DAGBagM allows for either continuous or binary nodes to be parent or child nodes. It employs a bootstrap aggregating strategy to reduce false positives in edge inference. At the same time, the aggregation procedure provides a flexible framework to robustly incorporate prior information on edges. Through extensive simulation experiments, we demonstrate that DAGBagM has superior performance compared to alternative strategies for modeling mixed types of nodes. In addition, DAGBagM is computationally more efficient than two competing methods. When applying DAGBagM to proteogenomic datasets from ovarian cancer studies, we identify potential protein biomarkers for platinum refractory/resistant response in ovarian cancer. DAGBagM is made available as a github repository at https://github.com/jie108/dagbagM. The online version contains supplementary material available at 10.1186/s12859-022-04864-y.
登录
查看更多内容
影响因子：
1.9
作者：
Han, Sung Won;Zhong, Hua
通讯作者：
Zhong, Hua
影响因子：
3.7
作者：
Ott C;Dorsch E;Fraunholz M;Straub S;Kozjak-Pavlovic V
通讯作者：
Kozjak-Pavlovic V
影响因子：
21.3
作者：
Boroughs LK;DeBerardinis RJ
通讯作者：
DeBerardinis RJ
影响因子：
4
作者：
Asvatourian, Vahe;Coutzac, Clelia;Lanoy, Emilie
通讯作者：
Lanoy, Emilie
影响因子：
5.8
作者：
Scutari, Marco
通讯作者：
Scutari, Marco