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

Scientific Reports

Volumn 7, Issue 1, 2017, Pages

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

^a EMORY UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b Department of Medicine   (United States)

^c School of Operations Research and Information Engineering   (United States)

^d EMORY UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; GENETICS; GENOMICS; HUMAN; INFORMATION PROCESSING; MORTALITY; NEOPLASM; PROCEDURES; PROGNOSIS; SOFTWARE; SURVIVAL; TREATMENT OUTCOME;

BAYES THEOREM; DATASETS AS TOPIC; DEEP LEARNING; GENOMICS; HUMANS; NEOPLASMS; NEURAL NETWORKS (COMPUTER); PROGNOSIS; SOFTWARE; SURVIVAL; TREATMENT OUTCOME;

EID: 85029478351     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/s41598-017-11817-6     Document Type: Article

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