Multi-omic machine learning predictor of breast cancer therapy response
Multi-omic machine learning predictor of breast cancer therapy response
About this item
Full title
Author / Creator
Sammut, Stephen-John , Crispin-Ortuzar, Mireia , Chin, Suet-Feung , Provenzano, Elena , Bardwell, Helen A. , Ma, Wenxin , Cope, Wei , Dariush, Ali , Dawson, Sarah-Jane , Abraham, Jean E. , Dunn, Janet , Hiller, Louise , Thomas, Jeremy , Cameron, David A. , Bartlett, John M. S. , Hayward, Larry , Pharoah, Paul D. , Markowetz, Florian , Rueda, Oscar M. , Earl, Helena M. and Caldas, Carlos
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment
1
. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy
2
. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic...
Alternative Titles
Full title
Multi-omic machine learning predictor of breast cancer therapy response
Authors, Artists and Contributors
Author / Creator
Crispin-Ortuzar, Mireia
Chin, Suet-Feung
Provenzano, Elena
Bardwell, Helen A.
Ma, Wenxin
Cope, Wei
Dariush, Ali
Dawson, Sarah-Jane
Abraham, Jean E.
Dunn, Janet
Hiller, Louise
Thomas, Jeremy
Cameron, David A.
Bartlett, John M. S.
Hayward, Larry
Pharoah, Paul D.
Markowetz, Florian
Rueda, Oscar M.
Earl, Helena M.
Caldas, Carlos
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8791834
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_8791834
Other Identifiers
ISSN
0028-0836,1476-4687
E-ISSN
1476-4687
DOI
10.1038/s41586-021-04278-5
