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Bayesian machine learning on CALGB/SWOG 80405 (Alliance) and PEAK data identify a heterogeneous landscape of clinical predictors of overall survival (OS) in different populations of metastatic colorectal cancer (mCRC).

Publication ,  Conference
Das, R; Ou, F; Washburn, C; Innocenti, F; Nixon, A; Lenz, H; Blanke, C; Niedzwiecki, D; Khalil, I; Harms, B; Venook, A
Published in: Ann Oncol
July 2019

Duke Scholars

Published In

Ann Oncol

DOI

EISSN

1569-8041

Publication Date

July 2019

Volume

30 Suppl 4

Start / End Page

iv116

Location

England

Related Subject Headings

  • Oncology & Carcinogenesis
  • 3211 Oncology and carcinogenesis
  • 3202 Clinical sciences
  • 1112 Oncology and Carcinogenesis
 

Citation

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Das, R., Ou, F., Washburn, C., Innocenti, F., Nixon, A., Lenz, H., … Venook, A. (2019). Bayesian machine learning on CALGB/SWOG 80405 (Alliance) and PEAK data identify a heterogeneous landscape of clinical predictors of overall survival (OS) in different populations of metastatic colorectal cancer (mCRC). In Ann Oncol (Vol. 30 Suppl 4, p. iv116). England. https://doi.org/10.1093/annonc/mdz156.019
Das, R., F. Ou, C. Washburn, F. Innocenti, A. Nixon, H. Lenz, C. Blanke, et al. “Bayesian machine learning on CALGB/SWOG 80405 (Alliance) and PEAK data identify a heterogeneous landscape of clinical predictors of overall survival (OS) in different populations of metastatic colorectal cancer (mCRC).” In Ann Oncol, 30 Suppl 4:iv116, 2019. https://doi.org/10.1093/annonc/mdz156.019.
Journal cover image

Published In

Ann Oncol

DOI

EISSN

1569-8041

Publication Date

July 2019

Volume

30 Suppl 4

Start / End Page

iv116

Location

England

Related Subject Headings

  • Oncology & Carcinogenesis
  • 3211 Oncology and carcinogenesis
  • 3202 Clinical sciences
  • 1112 Oncology and Carcinogenesis