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References
Оглавление1. Soh, K.P., Szczurek, E., Sakoparnig, T., Beerenwinkel, N., Predicting cancer type from tumour DNA signatures. Genome Med., 9, 104, 2017.
2. Hao, X., Luo, H., Krawczyk, M., Wei, W., Wang, W., Wang, J. et al., DNA methylation markers for diagnosis and prognosis of common cancers. Proc. Natl. Acad. Sci. U.S.A., 114, 28, 7414–19, 2017.
3. Yang, Z., Jin, M., Zhang, Z., Lu, J., Hao, K., Classification based on feature extraction for hepatocellular carcinoma diagnosis using high-throughput dna methylation sequencing data. Proc. Comput. Sci., 107, 412–417, 2017.
4. Rachman, A.A. and Rustam, Z., Cancer classification using Fuzzy C-Means with feature selection. 12th International Conference on Mathematics, Statistics, and Their Applications (ICMSA), pp. 31–34, 2016.
5. Ghosh, A. and De, R.K., Fuzzy Correlated Association Mining: Selecting altered associations among the genes, and some possible marker genes mediating certain cancers. Appl. Soft Comput., 38, 587–605, 2015.
6. Mao, Z.-Y., Cai, W.-S., Shao, X.-G., Selecting significant genes by randomization test for cancer classification using gene expression data. J. Biomed. Inform., 46, 4, 549–601, 2013.
7. Cao, J., Zhang, L., Wang, B., Li, F., Yang, J., A fast gene selection method for multi-cancer classification using multiple support vector data description. J. Biomed. Inform., 53, 381–389, 2015.
8. Dashtban, M. and Balafar, Gene selection for microarray cancer classification using a new evolutionary method employing artificial intelligence concepts. Genomics, 109, 2, 91–107, 2017.
9. Mirza, B., Wang, W., Wang, J., Choi, H., Chung, N.C., Ping, P., Machine Learning and Integrative Analysis of Biomedical Big Data. Genes, 10, 2, 87, 2019.
10. Hoadley, K.A. et al., Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin. Cell, 158, 929–944, 2014.
11. Yang, A. et al., Bayesian variable selection with sparse and correlation priors for high-dimensional data analysis. Comput. Stat., 32, 1–17, 2016.
12. Danaee, P., Ghaeini, R., Hendrix, D.A., A deep learning approach for cancer detection and relevant gene identification. Pac. Symp. Biocomput., 22, 219–229, 2017.
13. Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I., Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J., 13, 8–17, 2014.
14. Tabl, A.A., Alkhateeb, A., ElMaraghy, W., Rueda, L., Ngom, A., A machine learning approach for identifying gene biomarkers guiding the treatment of breast Cancer. Front. Genet., 10, 256, 2019.
15. Huang, S., Cai, N., Pacheco, P.P., Narrandes, S., Wang, Y., Xu, W., Applications of Support Vector Machine (SVM) Learning in Cancer Genomics. Cancer Genomics Proteomics, 15, 1, 41–51, 2018.
16. Rajaguru, H., Analysis of Decision Tree and K-Nearest Neighbor Algorithm in the Classification of Breast Cancer. Asian Pac. J. Cancer Prev., 20, 12, 3777–378, 2019.
17. Neha, K. and Verma, K., Volume 10, May-June 2019.A survey on various machine learning approaches used for breast cancer detection. IJARCS, 10, 3, 76–79, 2019.
18. Libbrecht, M.W. and Noble, W.S., Machine learning applications in genetics and genomics. Nat. Rev. Genet., 16, 321–332, 2015.
19. Gao, F., Wang, W., Tan, M. et al., DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. Oncogenesis, 8, 9, 44, 2019.
20. Sultana, J., Predicting Breast Cancer using Logistic Regression and Multi-Class Classifiers. Int. J. Eng. Technol.(UAE), 7, 4.20, 22–26, 2018.
21. Alkuhlani, A., Nassef, M., Farag, I., Multistage feature selection approach for high-dimensional cancer data. Soft Comput., 21, 6895–6906, 2017.
22. Kavitha, R.K., Ram, A.V., Anandu, S., Karthik, S., Kailas, S., Arjun, N.M., PCA-based gene selection for cancer classification. 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, India, pp. 1–4, 2018.
23. Wu, W. and Faisal, S., A data-driven principal component analysis-support vector machine approach for breast cancer diagnosis: Comparison and application. Trans. Inst. Meas. Control, 42, 7, 1301–1312, 2020.
24. Deshmukh, P., Design of Cloud Security in the EHR for Indian Healthcare Services. J. King Saud Univ. – Comp. Info. Sci., 29, 3, 281–287, 2017.
25. Langmead, B. and Nellore, A., Cloud computing for genomic data analysis and collaboration. Nat. Rev. Genet., 19, 4, 208–219, 2018.
1 *Corresponding author: hazrasubir@gmail.com