1 |
M.R. Boyd, K.D. Paull, Some practical considerations and applications of the national cancer institute in vitro anticancer drug discovery screen, Drug Dev. Res. 34 (1995) 91-109.
DOI
|
2 |
X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks, in: T. Yee Whye, T. Mike (Eds.), Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, PMLR, 2010, pp. 249-256.
|
3 |
G.E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, R.R. Salakhutdinov, Improving Neural Networks by Preventing Co-adaptation of Feature Detectors, 2012 arXiv preprint arXiv:12070580.
|
4 |
P. Meyer, V. Noblet, C. Mazzara, A. Lallement, Survey on deep learning for radiotherapy, Comput. Biol. Med. 98 (2018) 126-146.
DOI
|
5 |
D.G. Hirst, T. Robson, Molecular biology: the key to personalised treatment in radiation oncology? Br. J. Radiol. 83 (2010) 723-728.
DOI
|
6 |
A. Krizhevsky, I. Sutskever, G.E. Hinton, Imagenet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst. (2012) 1097-1105.
|
7 |
T.D. Pfister, W.C. Reinhold, K. Agama, S. Gupta, S.A. Khin, R.J. Kinders, et al., Topoisomerase I levels in the NCI-60 cancer cell line panel determined by validated ELISA and microarray analysis and correlation with inden-oisoquinoline sensitivity, Mol. Cancer Therapeut. 8 (2009) 1878-1884.
DOI
|
8 |
G.E. Dahl, T.N. Sainath, G.E. Hinton, Improving deep neural networks for LVCSR using rectified linear units and dropout, in: IEEE International Conference on Acoustics, Speech and Signal Processing 2013, 2013, pp. 8609-8613.
|
9 |
J.G. Scott, G. Sedor, P. Ellsworth, J.A. Scarborough, K.A. Ahmed, D.E. Oliver, et al., Pan-cancer prediction of radiotherapy benefit using genomic-adjusted radiation dose (GARD): a cohort-based pooled analysis, Lancet Oncol. 22 (2021) 1221-1229.
DOI
|
10 |
S.D. Bouffler, Evidence for variation in human radiosensitivity and its potential impact on radiological protection, Ann. ICRP 45 (2016) 280-289.
DOI
|
11 |
K. Ogawa, S. Murayama, M. Mori, Predicting the tumor response to radiotherapy using microarray analysis (Review), Oncol. Rep. 18 (2007) 1243-1248.
|
12 |
H.S. Kim, S.C. Kim, S.J. Kim, C.H. Park, H.C. Jeung, Y.B. Kim, et al., Identification of a radiosensitivity signature using integrative metaanalysis of published microarray data for NCI-60 cancer cells, BMC Genom. 13 (2012) 348.
DOI
|
13 |
J.F. Torres-Roca, S. Eschrich, H. Zhao, G. Bloom, J. Sung, S. McCarthy, et al., Prediction of radiosensitivity using a gene expression classifier, Cancer Res. 65 (2005) 7169-7176.
DOI
|
14 |
S. Ramaswamy, T.R. Golub, DNA microarrays in clinical oncology, J. Clin. Oncol. 20 (2002) 1932-1941.
DOI
|
15 |
L.J. Peters, W.A. Brock, J.D. Chapman, G. Wilson, Predictive assays of tumor radiocurability, Am. J. Clin. Oncol. 11 (1988) 275-287.
DOI
|
16 |
S. Eschrich, H. Zhang, H. Zhao, D. Boulware, J.H. Lee, G. Bloom, et al., Systems biology modeling of the radiosensitivity network: a biomarker discovery platform, Int. J. Radiat. Oncol. Biol. Phys. 75 (2009) 497-505.
DOI
|
17 |
M. Burkard, Integrating the NCI-60 data with "omics" for drug discovery, Drug Dev. Res. 73 (2012).
|
18 |
R.H. Shoemaker, The NCI60 human tumour cell line anticancer drug screen, Nat. Rev. Cancer 6 (2006) 813-823.
DOI
|
19 |
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, Proceedings of the IEEE conference on computer vision and pattern recognition (2016) 770-778.
|
20 |
A.C. Begg, F.A. Stewart, C. Vens, Strategies to improve radiotherapy with targeted drugs, Nat. Rev. Cancer 11 (2011) 239-253.
DOI
|
21 |
J.H. Oh, W. Choi, E. Ko, M. Kang, A. Tannenbaum, J.O. Deasy, PathCNN: interpretable convolutional neural networks for survival prediction and pathway analysis applied to glioblastoma, Bioinformatics 37 (2021) i443-i450.
DOI
|
22 |
V. Nair, G.E. Hinton, Rectified linear units improve restricted Boltzmann machines, in: Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010, pp. 807-814.
|
23 |
D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. van den Driessche, et al., Mastering the game of Go with deep neural networks and tree search, Nature 529 (2016) 484-489.
DOI
|
24 |
S.J. Pan, Q. Yang, A survey on transfer learning, IEEE Trans. Knowl. Data Eng. 22 (2010) 1345-1359.
DOI
|
25 |
C. Zhang, L. Girard, A. Das, S. Chen, G. Zheng, K. Song, Nonlinear quantitative radiation sensitivity prediction model based on NCI-60 cancer cell lines, Sci. World J. 2014 (2014) 903602.
|
26 |
A.L. Maas, A.Y. Hannun, A.Y. Ng, Rectifier nonlinearities improve neural network acoustic models, Proc icml: Cites (2013) 3.
|
27 |
J. Khan, J.S. Wei, M. Ringner, L.H. Saal, M. Ladanyi, F. Westermann, et al., Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks, Nat. Med. 7 (2001) 673-679.
DOI
|
28 |
S.A. Amundson, K.T. Do, L.C. Vinikoor, R.A. Lee, C.A. Koch-Paiz, J. Ahn, et al., Integrating global gene expression and radiation survival parameters across the 60 cell lines of the National Cancer Institute Anticancer Drug Screen, Cancer Res. 68 (2008) 415-424.
DOI
|