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DESCRIBE <http://purl.uniprot.org/SHA-384/53AE8EF1D70D6928943AF9AF4E86386F44C3F636376FBF40029752C6DA2F62FCD97A5BB29615A4ABE2A049FA56D373C0>
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http://purl.uniprot.org/SHA-384/53AE8EF1D70D6928943AF9AF4E86386F44C3F636376FBF40029752C6DA2F62FCD97A5BB29615A4ABE2A049FA56D373C0
http://www.w3.org/1999/02/22-rdf-syntax-ns#type
http://purl.uniprot.org/core/Annotation
http://purl.uniprot.org/SHA-384/53AE8EF1D70D6928943AF9AF4E86386F44C3F636376FBF40029752C6DA2F62FCD97A5BB29615A4ABE2A049FA56D373C0
http://www.w3.org/2000/01/rdf-schema#comment
"Identifying key genes related to inflammasome in severe COVID-19 patients based on a joint model with random forest and artificial neural network."
xsd:string
http://purl.uniprot.org/uniprot/#_0C329DCFBD54782FF8A1151C1699B747A1BDB7CAD107ED13D19150F05F85138088A141D9AEAD4DAF606350FF003A5F65
http://www.w3.org/1999/02/22-rdf-syntax-ns#subject
http://purl.uniprot.org/SHA-384/53AE8EF1D70D6928943AF9AF4E86386F44C3F636376FBF40029752C6DA2F62FCD97A5BB29615A4ABE2A049FA56D373C0
http://purl.uniprot.org/uniprot/Q9BY34
http://purl.uniprot.org/core/mappedAnnotation
http://purl.uniprot.org/SHA-384/53AE8EF1D70D6928943AF9AF4E86386F44C3F636376FBF40029752C6DA2F62FCD97A5BB29615A4ABE2A049FA56D373C0
http://purl.uniprot.org/uniprot/#_Q9BY34-mappedCitation-37113134
http://purl.uniprot.org/core/mappedAnnotation
http://purl.uniprot.org/SHA-384/53AE8EF1D70D6928943AF9AF4E86386F44C3F636376FBF40029752C6DA2F62FCD97A5BB29615A4ABE2A049FA56D373C0