prognosis estimation
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2021 ◽  
Vol 116 (1) ◽  
pp. S1480-S1480
Author(s):  
Dong Joo Seo ◽  
Aysha Aslam ◽  
Praneet Srisailam ◽  
Navya Kirla


2021 ◽  
Author(s):  
Marie PAVAGEAU ◽  
Louis REBAUD ◽  
Daphne MOREL ◽  
Stergios CHRISTODOULIDIS ◽  
Eric DEUTSCH ◽  
...  

RNA sequencing (RNAseq) analysis offers a tumor centered approach of growing interest for personalizing cancer care. However, existing methods , including deep learning models, struggle to reach satisfying performances on survival prediction based upon pan-cancer RNAseq data. Here, we present DeepOS, a novel deep learning model that predicts overall survival (OS) from pancancer RNAseq with a concordance index of 0.715 and a survival AUC of 0.752 across 33 TCGA tumor types whilst tested on an unseen test cohort. DeepOS notably uses (i) prior biological knowledge to condense inputs dimensionality, (ii) transfer learning to enlarge its training capacity through pretraining on organ prediction, and (iii) mean squared error adapted to survival loss function; all of which contributed to improve the model performances. Interpretation showed that DeepOS learned biologically relevant prognosis biomarkers. Altogether, DeepOS achieved unprecedented and consistent performances on pan-cancer prognosis estimation from individual RNA-seq data.



2020 ◽  
Author(s):  
Alexandros Laios ◽  
Angeliki Katsenou ◽  
Yong Tan ◽  
Mohamed Otify ◽  
Angelika Kaufmann ◽  
...  


Author(s):  
Alexandros Laios ◽  
Mohamed Otify ◽  
Angeliki Katsenou ◽  
Diederick De Jong ◽  
Georgios Tehophilou


2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Vincent Grollemund ◽  
Gaétan Le Chat ◽  
Marie-Sonia Secchi-Buhour ◽  
François Delbot ◽  
Jean-François Pradat-Peyre ◽  
...  


2020 ◽  
Vol 216 (1) ◽  
pp. 152763 ◽  
Author(s):  
Xueping Tao ◽  
Yongfu Shao ◽  
Rongdan Lu ◽  
Qihua Ye ◽  
Bingxiu Xiao ◽  
...  


2019 ◽  
Vol 60 (3) ◽  
pp. 708-714 ◽  
Author(s):  
Kwang Sun Ryu ◽  
Jang-Whan Bae ◽  
Myung Ho Jeong ◽  
Myeong-Chan Cho ◽  
Keun-Ho Ryu ◽  
...  


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