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Machine learning from Pseudomonas aeruginosa transcriptomes identifies independently modulated sets of genes associated with known transcriptional regulators.
Rajput, Akanksha; Tsunemoto, Hannah; Sastry, Anand V; Szubin, Richard; Rychel, Kevin; Sugie, Joseph; Pogliano, Joe; Palsson, Bernhard O.
Afiliación
  • Rajput A; Department of Bioengineering, University of California, San Diego, La Jolla, USA.
  • Tsunemoto H; Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA.
  • Sastry AV; Department of Bioengineering, University of California, San Diego, La Jolla, USA.
  • Szubin R; Department of Bioengineering, University of California, San Diego, La Jolla, USA.
  • Rychel K; Department of Bioengineering, University of California, San Diego, La Jolla, USA.
  • Sugie J; Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA.
  • Pogliano J; Division of Biological Sciences, University of California San Diego, La Jolla, CA 92093, USA.
  • Palsson BO; Department of Bioengineering, University of California, San Diego, La Jolla, USA.
Nucleic Acids Res ; 50(7): 3658-3672, 2022 04 22.
Article en En | MEDLINE | ID: mdl-35357493
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Pseudomonas aeruginosa / Transcriptoma Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nucleic Acids Res Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos
Texto completo
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Pseudomonas aeruginosa / Transcriptoma Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nucleic Acids Res Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos