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The Scientific World Journal
Volume 2014, Article ID 903602, 11 pages
http://dx.doi.org/10.1155/2014/903602
Research Article

Nonlinear Quantitative Radiation Sensitivity Prediction Model Based on NCI-60 Cancer Cell Lines

1School of Chemical Engineering and Technology, Tianjin University, Tianjin 300072, China
2Hamon Center for Therapeutic Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
3Department of Pharmacology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
4Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
5Department of Clinical Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA

Received 7 April 2014; Revised 20 May 2014; Accepted 21 May 2014; Published 17 June 2014

Academic Editor: Yudong Cai

Copyright © 2014 Chunying Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [2 citations]

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  • Haitao Yang, Shaoyu Li, Hongyan Cao, Chichen Zhang, and Yuehua Cui, “Predicting disease trait with genomic data: a composite kernel approach,” Briefings in Bioinformatics, pp. bbw043, 2016. View at Publisher · View at Google Scholar
  • Shi-Xiang Wang, Ling Wang, Fei Zhang, and Kai Song, “Pattern recognition of the lung squamous cell carcinoma tumor progression classification model and signature genes identification,” Progress in Biochemistry and Biophysics, vol. 43, no. 1, pp. 63–74, 2016. View at Publisher · View at Google Scholar