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)

R. Das; F. Ou; C. Washburn; F. Innocenti; A. Nixon; H. Lenz; C. Blanke; D. Niedzwiecki; I. Khalil; B. Harms; A. Venook

doi:10.1093/annonc/mdz156.019

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)

R. Das, F. Ou, C. Washburn, F. Innocenti, A. Nixon, H. Lenz, C. Blanke, D. Niedzwiecki, I. Khalil, B. Harms, A. Venook

Quantitative Health Sciences

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Original language	English (US)
Pages (from-to)	iv116
Journal	Annals of oncology : official journal of the European Society for Medical Oncology
Volume	30
DOIs	https://doi.org/10.1093/annonc/mdz156.019
State	Published - Jul 1 2019

ASJC Scopus subject areas

Hematology
Oncology

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Das, R., Ou, F., Washburn, C., Innocenti, F., Nixon, A., Lenz, H., Blanke, C., Niedzwiecki, D., Khalil, I., Harms, B., & 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). Annals of oncology : official journal of the European Society for Medical Oncology, 30, iv116. https://doi.org/10.1093/annonc/mdz156.019

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). / Das, R.; Ou, F.; Washburn, C. et al.
In: Annals of oncology : official journal of the European Society for Medical Oncology, Vol. 30, 01.07.2019, p. iv116.

Research output: Contribution to journal › Article › peer-review

Das, R, Ou, F, Washburn, C, Innocenti, F, Nixon, A, Lenz, H, Blanke, C, Niedzwiecki, D, Khalil, I, Harms, B & 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)', Annals of oncology : official journal of the European Society for Medical Oncology, vol. 30, pp. iv116. https://doi.org/10.1093/annonc/mdz156.019

Das R, Ou F, Washburn C, Innocenti F, Nixon A, Lenz H 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). Annals of oncology : official journal of the European Society for Medical Oncology. 2019 Jul 1;30:iv116. doi: 10.1093/annonc/mdz156.019

Das, R. ; Ou, F. ; Washburn, C. 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: Annals of oncology : official journal of the European Society for Medical Oncology. 2019 ; Vol. 30. pp. iv116.

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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)

ASJC Scopus subject areas

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