Inherently interpretable position-aware convolutional motif kernel networks for biological sequencin...
Inherently interpretable position-aware convolutional motif kernel networks for biological sequencing data
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London: Nature Publishing Group UK
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English
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London: Nature Publishing Group UK
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Artificial neural networks show promising performance in detecting correlations within data that are associated with specific outcomes. However, the black-box nature of such models can hinder the knowledge advancement in research fields by obscuring the decision process and preventing scientist to fully conceptualize predicted outcomes. Furthermore...
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Inherently interpretable position-aware convolutional motif kernel networks for biological sequencing data
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TN_cdi_doaj_primary_oai_doaj_org_article_a9b7edc8a14a4f03a9d7513700f7e21e
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_a9b7edc8a14a4f03a9d7513700f7e21e
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ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-023-44175-7
