On the Methods of Artificial Intelligence for Analysis of Oncological Data-Reference-Cited by-同舟云学术

On the Methods of Artificial Intelligence for Analysis of Oncological Data

Published:2020-09 Issue:5 Volume:54 Page:255-259
ISSN:0005-1055
Container-title:Automatic Documentation and Mathematical Linguistics
language:en
Short-container-title:Autom. Doc. Math. Linguist.

Author:

Chebanov D. K.,Mikhaylova I. N.

Publisher

Allerton Press

Link

http://link.springer.com/content/pdf/10.3103/S0005105520050027.pdf

Reference32 articles.

1. Nindrea, R.D., Aryandono, T., Lazuardi, L., and Dwiprahasto, I., Diagnostic accuracy of different machine learning algorithms for breast cancer risk calculation: A meta-analysis, Asian Pac. J. Cancer Prev., 2018, vol. 19, no. 7, pp. 1747–1752.

2. Xie, G., Dong, C., Kong, Y., Zhong, J.F., Li, M., and Wang, K., Group lasso regularized deep learning for cancer prognosis from multi-omics and clinical features, Genes (Basel), 2019, vol. 10, no. 3. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6471789/.

3. Chen, H., Kodell, R.L., Cheng, K.F., et al., Assessment of performance of survival prediction models for cancer prognosis, BMC Med. Res. Methodol., 2012, vol. 12, p. 102. https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-12-102.

4. Su, J., Zhang, Y., Su, H., Zhang, C., and Li, W., A recurrence model for laryngeal cancer based on SVM and gene function clustering, Acta Otolaryngol., 2017, vol. 137, no. 5, pp. 557–562.

5. Chen, R., Garapati, S., Wu, D., Ko, S., Falk, S., Dierov, D., Stasiw, A., Opong, A.S., and Carson, K.R., Machine learning based predictive model of 5-year survival in multiple myeloma autologous transplant patients, Blood, 2019, vol. 134. https://ashpublications.org/blood/article/134/Supplement_1/2156/427904/Machine-Learning-Based-Predictive-Model-of-5-Year.