scholarly journals Network information improves cancer outcome prediction

2012 ◽  
Vol 15 (4) ◽  
pp. 612-625 ◽  
Author(s):  
J. Roy ◽  
C. Winter ◽  
Z. Isik ◽  
M. Schroeder
Keyword(s):  
Outcome Prediction ◽  
Network Information ◽  
Cancer Outcome

scholarly journals Ensemble classifier based on context specific miRNA regulation modules: a new method for cancer outcome prediction

2013 ◽  
Vol 14 (Suppl 12) ◽  
pp. S6 ◽  
Author(s):  
Xionghui Zhou ◽  
Juan Liu ◽  
Xinghuo Ye ◽  
Wei Wang ◽  
Jianghui Xiong
Keyword(s):  
Outcome Prediction ◽  
Ensemble Classifier ◽  
New Method ◽  
Mirna Regulation ◽  
Cancer Outcome ◽  
Context Specific

scholarly journals A data-driven interactome of synergistic genes improves network-based cancer outcome prediction

2019 ◽  
Vol 15 (2) ◽  
pp. e1006657 ◽  
Author(s):  
Amin Allahyar ◽  
Joske Ubels ◽  
Jeroen de Ridder
Keyword(s):  
Outcome Prediction ◽  
Data Driven ◽  
Cancer Outcome

Validation of a Rectal Cancer Outcome Prediction Model in Routine Chinese Patients

2014 ◽  
Vol 90 (1) ◽  
pp. S387-S388
Author(s):  
L. Shen ◽  
J. Van Soest ◽  
J. Yu ◽  
J. Wang ◽  
W. Hu ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Prediction Model ◽  
Outcome Prediction ◽  
Chinese Patients ◽  
Cancer Outcome

scholarly journals Determination of Minimum Training Sample Size for Microarray-Based Cancer Outcome Prediction–An Empirical Assessment

PLoS ONE ◽  
2013 ◽  
Vol 8 (7) ◽  
pp. e68579 ◽  
Author(s):  
Li Shao ◽  
Xiaohui Fan ◽  
Ningtao Cheng ◽  
Leihong Wu ◽  
Yiyu Cheng
Keyword(s):  
Sample Size ◽  
Outcome Prediction ◽  
Training Sample ◽  
Empirical Assessment ◽  
Training Sample Size ◽  
Cancer Outcome

scholarly journals Comprehensive Evaluation of Composite Gene Features in Cancer Outcome Prediction

2014 ◽  
Vol 13s3 ◽  
pp. CIN.S14028
Author(s):  
Dezhi Hou ◽  
Mehmet Koyutürk
Keyword(s):  
Outcome Prediction ◽  
Comprehensive Evaluation ◽  
Test Procedures ◽  
Individual Gene ◽  
Cancer Subtypes ◽  
Cancer Types ◽  
Cancer Outcome ◽  
Robust Prediction ◽  
Microarray Datasets ◽  
Gene Feature

Owing to the heterogeneous and continuously evolving nature of cancers, classifiers based on the expression of individual genes usually do not result in robust prediction of cancer outcome. As an alternative, composite gene features that combine functionally related genes have been proposed. It is expected that such features can be more robust and reproducible since they can capture the alterations in relevant biological processes as a whole and may be less sensitive to fluctuations in the expression of individual genes. Various algorithms have been developed for the identification of composite features and inference of composite gene feature activity, which all claim to improve the prediction accuracy. However, because of the limitations of test datasets incorporated by each individual study and inconsistent test procedures, the results of these studies are sometimes conflicting and unproducible. For this reason, it is difficult to have a comprehensive understanding of the prediction performance of composite gene features, particularly across different cancers, cancer subtypes, and cohorts. In this study, we implement various algorithms for the identification of composite gene features and their utilization in cancer outcome prediction, and perform extensive comparison and evaluation using seven microarray datasets covering two cancer types and three different phenotypes. Our results show that, while some algorithms outperform others for certain classification tasks, no single algorithm consistently outperforms other algorithms and individual gene features.

scholarly journals Validation of a rectal cancer outcome prediction model with a cohort of Chinese patients

Oncotarget ◽  
2015 ◽  
Vol 6 (35) ◽  
pp. 38327-38335 ◽  
Author(s):  
Lijun Shen ◽  
Johan van Soest ◽  
Jiazhou Wang ◽  
Jialu Yu ◽  
Weigang Hu ◽  
...  
Keyword(s):  
Rectal Cancer ◽  
Prediction Model ◽  
Outcome Prediction ◽  
Chinese Patients ◽  
Cancer Outcome

scholarly journals Deep learning in head & neck cancer outcome prediction

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
André Diamant ◽  
Avishek Chatterjee ◽  
Martin Vallières ◽  
George Shenouda ◽  
Jan Seuntjens
Keyword(s):  
Deep Learning ◽  
Neck Cancer ◽  
Outcome Prediction ◽  
Head Neck Cancer ◽  
Cancer Outcome ◽  
Head Neck
Sign in / Sign up

Export Citation Format

Share Document