Leveraging multiple data types for improved compound-kinase bioactivity prediction
Leveraging multiple data types for improved compound-kinase bioactivity prediction
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London: Nature Publishing Group UK
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English
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Publisher
London: Nature Publishing Group UK
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Contents
Machine learning provides efficient ways to map compound-kinase interactions. However, diverse bioactivity data types, including single-dose and multi-dose-response assay results, present challenges. Traditional models utilize only multi-dose data, overlooking information contained in single-dose measurements. Here, we propose a machine learning me...
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Leveraging multiple data types for improved compound-kinase bioactivity prediction
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TN_cdi_doaj_primary_oai_doaj_org_article_4d18f6b76b8e4d209b0b977e65748d7a
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_4d18f6b76b8e4d209b0b977e65748d7a
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ISSN
2041-1723
E-ISSN
2041-1723
DOI
10.1038/s41467-024-52055-5
