Your browser doesn't support javascript.
loading
A machine learning and directed network optimization approach to uncover TP53 regulatory patterns.
Triantafyllidis, Charalampos P; Barberis, Alessandro; Hartley, Fiona; Cuervo, Ana Miar; Gjerga, Enio; Charlton, Philip; van Bijsterveldt, Linda; Rodriguez, Julio Saez; Buffa, Francesca M.
Affiliation
  • Triantafyllidis CP; Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK.
  • Barberis A; Department of Epidemiology & Biostatistics, School of Public Health, Imperial College London, London, UK.
  • Hartley F; Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK.
  • Cuervo AM; Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK.
  • Gjerga E; Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK.
  • Charlton P; Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK.
  • van Bijsterveldt L; Heidelberg University, Faculty of Medicine, Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany.
  • Rodriguez JS; Department of Oncology, Medical Sciences Division, University of Oxford, Oxford, UK.
  • Buffa FM; MRC Laboratory of Molecular Biology, Francis Crick Avenue, Cambridge, UK.
iScience ; 26(12): 108291, 2023 Dec 15.
Article in En | MEDLINE | ID: mdl-38047081
Key words
Fulltext
Add to My VHL
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: IScience Year: 2023 Document type: Article Affiliation country: United kingdom Country of publication: United States
Fulltext
Add to My VHL
Print
XML
PubMed Links
Search on Google

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: IScience Year: 2023 Document type: Article Affiliation country: United kingdom Country of publication: United States