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Improved prediction of radiation pneumonitis by combining biological and radiobiological parameters using a data-driven Bayesian network analysis

Translational Oncology(2022)10.1016/j.tranon.2022.101428Source: DataRank Database

Improved prediction of radiation pneumonitis by combining biological and radiobiological parameters using a data-driven Bayesian network analysis is a research paper published in Translational Oncology (2022). On theSindex it has a DataRank of 0. It has been cited 9 times.

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Open Access9 citations

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›Methodology & internals

datarank_citation_only_1hop_v6· scope data_onlyMethodology

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Enrichment:Funding (3 grants)OA: goldTopics (11)IDs (PubMed)SDGs (1)

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Context only (not used in score)

Findable (1/2)

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Accessible (1/2)

Open Access

Interoperable (0/2)

Reusable (0/3)

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Authors (22)

David Karnak,Ming Tang ORCID,Ralph Jiang ORCID,Yi Luo ORCID,Philip S. Boonstra ORCID

Live enrichment

PubMed:35460942

National Institutes of Health

Grant: P01 CA059827

National Institutes of Health

Grant: 2P01CA059827-11A1

COMPUTER SOFTWARE SUPPORT

NCI NIH HHS

Grant: R01 CA233487

goldLicense: cc-by-nc-nd

publisherhttps://doi.org/10.1016/j.tranon.2022.101428

publisherhttps://api.elsevier.com/content/article/PII:S1936523322000870?httpAccept=text/xml

publisherhttps://api.elsevier.com/content/article/PII:S1936523322000870?httpAccept=text/plain

repositoryhttps://doaj.org/article/14a26493ad054b57b6d1c4139b2ae385

repositoryhttps://www.ncbi.nlm.nih.gov/pmc/articles/9046881

Fields of Study

0301 basic medicine03 medical and health sciences0302 clinical medicine

Keywords

Neoplasms. Tumors. Oncology. Including cancer and carcinogensData-driven Bayesian network (DD-BN) analysisRadiation pneumonitisMicroRNA (miRNA)Inflammatory cytokinesTumor necrosis factor alpha (TNFα) signaling, nuclear factor kappa B (NFκB)RC254-282Original Research

Sustainable Development Goals

SDG 3: 3. Good health