Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models-Reference-Cited by-同舟云学术

Predicting clinical outcomes from large scale cancer genomic profiles with deep survival models

Published:2017-09-15 Issue:1 Volume:7 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Yousefi Safoora,Amrollahi Fatemeh,Amgad Mohamed,Dong Chengliang,Lewis Joshua E.,Song Congzheng,Gutman David A.,Halani Sameer H.,Velazquez Vega Jose Enrique,Brat Daniel J.,Cooper Lee A. D.^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Link

http://www.nature.com/articles/s41598-017-11817-6.pdf

Reference36 articles.

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4. Bartlett, J. M. et al. Mammostrat as a tool to stratify breast cancer patients at risk of recurrence during endocrine therapy. Breast Cancer Res 12, R47, doi: https://doi.org/10.1186/bcr2604 (2010).

5. Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V. & Fotiadis, D. I. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 13, 8–17, doi: https://doi.org/10.1016/j.csbj.2014.11.005 (2015).

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