DAGBagM: learning directed acyclic graphs of mixed variables with an application to identify protein biomarkers for treatment response in ovarian cancer.
- DOI
- 10.1186/s12859-022-04864-y
- Published
- 2022-08-05
- Container
- BMC Bioinformatics
- Publisher
- Not recorded
- Open access
- yes
Credibility signals
uncertain Score 53/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
Show all credibility signals
- cautionDOI registered: No matching Crossref record was present in this response.
- cautionDOI resolves: No matching Crossref record was present in this response.
- supportingDirectory of Open Access Journals: A matching record was returned by DOAJ.
- not scoredMEDLINE indexed: Not checked or no result supplied; no credibility inference made.
- not scoredOpenAlex core source: Not checked or no result supplied; no credibility inference made.
- not scoredKnown publisher allow-list: Not checked or no result supplied; no credibility inference made.
- not scoredROR affiliation: Not checked or no result supplied; no credibility inference made.
- not scoredRetraction Watch retraction: No retraction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch expression of concern: No expression of concern notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch correction: No correction notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- not scoredRetraction Watch reinstatement: No reinstatement notice matched this DOI in the deployed snapshot. No matching event found; coverage may be incomplete.
- supportingOpen access status: Normalized open-access status: open.
- not scoredPublication license: Not checked or no result supplied; no credibility inference made.
- not scoredPublication version: A publication version was supplied but is not scored.
- cautionMetadata completeness: 5 of 6 scored descriptive metadata groups are present; missing fields increase uncertainty.
Cite this work
BibTeX
@article{allodium:10.1186/s12859-022-04864-y,
title = {DAGBagM: learning directed acyclic graphs of mixed variables with an application to identify protein biomarkers for treatment response in ovarian cancer.},
author = {Chowdhury S and Wang R and Yu Q and Huntoon CJ and Karnitz LM and Kaufmann SH and Gygi SP and Birrer MJ and Paulovich AG and Peng J and Wang P.},
year = {2022},
journal = {BMC Bioinformatics},
doi = {10.1186/s12859-022-04864-y},
url = {https://doi.org/10.1186/s12859-022-04864-y}
}RIS
TY - JOUR TI - DAGBagM: learning directed acyclic graphs of mixed variables with an application to identify protein biomarkers for treatment response in ovarian cancer. AU - Chowdhury S AU - Wang R AU - Yu Q AU - Huntoon CJ AU - Karnitz LM AU - Kaufmann SH AU - Gygi SP AU - Birrer MJ AU - Paulovich AG AU - Peng J AU - Wang P. PY - 2022 JO - BMC Bioinformatics DO - 10.1186/s12859-022-04864-y UR - https://doi.org/10.1186/s12859-022-04864-y ER -
APA
S, C., R, W., Q, Y., CJ, H., LM, K., SH, K., SP, G., MJ, B., AG, P., J, P., & P., W. (2022). DAGBagM: learning directed acyclic graphs of mixed variables with an application to identify protein biomarkers for treatment response in ovarian cancer.. BMC Bioinformatics. https://doi.org/10.1186/s12859-022-04864-y
Source records
- europe-pmc · retrieved 2026-09-26T04:00:09.104Z
- doaj · retrieved 2026-09-26T04:00:09.083Z