http://rdf.ncbi.nlm.nih.gov/pubchem/reference/34919212
Outgoing Links
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contentType
Journal Article|Research Support, Non-U.S. Gov't
endingPage
9
issn
1046-2023
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publicationName
Methods (San Diego, Calif.)
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bibliographicCitation
Chen S, Yang Y, Zhou H, Sun Q, Su R. DNN-PNN: A parallel deep neural network model to improve anticancer drug sensitivity. Methods. 2023 Jan;209():1–9. doi: 10.1016/j.ymeth.2022.11.002. PMID: 36410694.
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date
202301
identifier
https://pubmed.ncbi.nlm.nih.gov/36410694
https://doi.org/10.1016/j.ymeth.2022.11.002
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https://portal.issn.org/resource/ISSN/1046-2023
language
English
source
https://pubmed.ncbi.nlm.nih.gov/
https://www.crossref.org/
title
DNN-PNN: A parallel deep neural network model to improve anticancer drug sensitivity
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Total number of triples:
27
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