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

Abstract Background 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 m...

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Bibliographic Details
Main Authors: Shrabanti Chowdhury, Ru Wang, Qing Yu, Catherine J. Huntoon, Larry M. Karnitz, Scott H. Kaufmann, Steven P. Gygi, Michael J. Birrer, Amanda G. Paulovich, Jie Peng, Pei Wang
Format: Article
Language:English
Published: BMC 2022-08-01
Series:BMC Bioinformatics
Subjects:
Proteomics
Sensitive and resistant/refractory
Hill climbing
Bootstrap aggregation
Online Access:https://doi.org/10.1186/s12859-022-04864-y

Internet

https://doi.org/10.1186/s12859-022-04864-y

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