reference/20376286

http://rdf.ncbi.nlm.nih.gov/pubchem/reference/20376286

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issn	1532-0464
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publicationName	Journal of Biomedical Informatics
startingPage	103957
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bibliographicCitation	De Angeli K, Gao S, Danciu I, Durbin EB, Wu X, Stroup A, Doherty J, Schwartz S, Wiggins C, Damesyn M, Coyle L, Penberthy L, Tourassi GD, Yoon H. Class imbalance in out-of-distribution datasets: Improving the robustness of the TextCNN for the classification of rare cancer types. Journal of Biomedical Informatics. 2022 Jan;125():103957. doi: 10.1016/j.jbi.2021.103957.
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date	202201
identifier	https://doi.org/10.1016/j.jbi.2021.103957 https://pubmed.ncbi.nlm.nih.gov/34823030 https://pubmed.ncbi.nlm.nih.gov/PMC9274264
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language	English
source	https://www.crossref.org/ https://pubmed.ncbi.nlm.nih.gov/
title	Class imbalance in out-of-distribution datasets: Improving the robustness of the TextCNN for the classification of rare cancer types
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