Nonlinear Quantitative Radiation Sensitivity Prediction Model Based on NCI-60 Cancer Cell Lines
Joint Authors
Zhang, Chunying
Girard, Luc
Das, Amit
Chen, Sun
Zheng, Guangqiang
Song, Kai
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-16
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
We proposed a nonlinear model to perform a novel quantitative radiation sensitivity prediction.
We used the NCI-60 panel, which consists of nine different cancer types, as the platform to train our model.
Important radiation therapy (RT) related genes were selected by significance analysis of microarrays (SAM).
Orthogonal latent variables (LVs) were then extracted by the partial least squares (PLS) method as the new compressive input variables.
Finally, support vector machine (SVM) regression model was trained with these LVs to predict the SF2 (the surviving fraction of cells after a radiation dose of 2 Gy γ -ray) values of the cell lines.
Comparison with the published results showed significant improvement of the new method in various ways: (a) reducing the root mean square error (RMSE) of the radiation sensitivity prediction model from 0.20 to 0.011; and (b) improving prediction accuracy from 62% to 91%.
To test the predictive performance of the gene signature, three different types of cancer patient datasets were used.
Survival analysis across these different types of cancer patients strongly confirmed the clinical potential utility of the signature genes as a general prognosis platform.
The gene regulatory network analysis identified six hub genes that are involved in canonical cancer pathways.
American Psychological Association (APA)
Zhang, Chunying& Girard, Luc& Das, Amit& Chen, Sun& Zheng, Guangqiang& Song, Kai. 2014. Nonlinear Quantitative Radiation Sensitivity Prediction Model Based on NCI-60 Cancer Cell Lines. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1051528
Modern Language Association (MLA)
Zhang, Chunying…[et al.]. Nonlinear Quantitative Radiation Sensitivity Prediction Model Based on NCI-60 Cancer Cell Lines. The Scientific World Journal No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1051528
American Medical Association (AMA)
Zhang, Chunying& Girard, Luc& Das, Amit& Chen, Sun& Zheng, Guangqiang& Song, Kai. Nonlinear Quantitative Radiation Sensitivity Prediction Model Based on NCI-60 Cancer Cell Lines. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1051528
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1051528