References

Ayesha, A.K., T. Hyodo, E. Asano, N. Sato and M.A. Mansour et al., 2016. UBE2S is associated with malignant characteristics of breast cancer cells. Tumor Biol., 37: 763-772.
CrossRef  |  PubMed  |  Direct Link  |  

Bader, A.G. and P. Lammers, 2011. The therapeutic potential of microRNAs. Innovations Pharm. Technol., 1: 52-55.

Culhane, A.C. and J. Quackenbush, 2009. Confounding effects in: A six-gene signature predicting breast cancer lung metastasis. Cancer Res., 69: 7480-7485.
PubMed  |  Direct Link  |  

Dy, J.G. and C.E. Bradley, 2004. Feature selection for unsupervised learning. J. Mach. Learn. Res., 5: 845-889.
Direct Link  |  

Eliseeva, I.A., E.R. Kim, S.G. Guryanov, L.P. Ovchinnikov and D.N. Lyabin, 2011. Y-box-binding protein 1 (YB-1) and its functions. Biochemistry (Moscow), 76: 1402-1433.
CrossRef  |  Direct Link  |  

Garro, B.A., K. Rodriguez and R.A. Vazquez, 2016. Classification of DNA microarrays using artificial neural networks and ABC algorithm. Appl. Soft Comput., 38: 548-560.
Direct Link  |  

Hanahan, D. and R.A. Weinberg, 2011. Hallmarks of cancer: The next generation. Cell, 144: 646-674.
CrossRef  |  PubMed  |  Direct Link  |  

Hayes, J., P.P. Peruzzi and S. Lawler, 2014. MicroRNAs in cancer: Biomarkers, functions and therapy. Trends Mol. Med., 20: 460-469.
Direct Link  |  

Jeselsohn, R., G. Buchwalter, C.D. Angelis, M. Brown and R. Schiff, 2015. ESR1 mutations a mechanism for acquired endocrine resistance in breast cancer. Nat. Rev. Clin. Oncol., 12: 573-583.
CrossRef  |  PubMed  |  Direct Link  |  

Kira, K. and L. Rendell, 1992. A practical approach to feature selection. Proceedings of the 9th International Workshop on Machine Learning, July 1-3, 1992, California: Morgan Kaufmann, pp: 249-256.

Koller, D. and M. Sahami, 1996. Toward optimal feature selection. Proceedings of the International Conference on Machine Learning, July 3-6, 1996, Bari, Italy, pp: 284-292.

Landemaine, T., A. Jackson, A. Bellahcene, N. Rucci and S. Sin et al., 2008. A six-gene signature predicting breast cancer lung metastasis. Cancer Res., 68: 6092-6099.
Direct Link  |  

Lu, J., G. Getz, E.A. Miska, E. Alvarez-Saavedra and J. Lamb et al., 2005. MicroRNA expression profiles classify human cancers. Nature, 435: 834-838.
CrossRef  |  PubMed  |  Direct Link  |  

Lu, J., G. Getz, E.A. Miska, E. Alvarez-Saavedra and J. Lamb et al., 2005. MicroRNA expression profiles classify human cancers. Nature, 435: 834-838.
CrossRef  |  PubMed  |  Direct Link  |  

Mitra, P., C.A. Murthy and S.K. Pal, 2002. Unsupervised feature selection using feature similarity. IEEE Trans. Pattern. Anal. Mach. Intell., 24: 301-312.
CrossRef  |  Direct Link  |  

Muscarella, L.A., V. D’Alessandro, A.L. Torre, M. Copetti and A.D. Cata et al., 2011. Gene expression of somatostatin receptor subtypes SSTR2a, SSTR3 and SSTR5 in peripheral blood of neuroendocrine lung cancer affected patients. Cell. Oncol., 34: 435-441.
CrossRef  |  PubMed  |  Direct Link  |  

Oesterreich, S. and N.E. Davidson, 2013. The search for ESR1 mutations in breast cancer. Nat. Genet., 45: 1415-1416.
CrossRef  |  PubMed  |  Direct Link  |  

Sokilde, R., M. Vincent, A.K. Moller, A. Hansen and P.E. Hoiby et al., 2014. Efficient identification of miRNAs for classification of tumor origin. J. Mol. Diagn., 16: 106-115.
CrossRef  |  PubMed  |  Direct Link  |  

Song, L., A. Smola, A. Gretton, K.M. Borgwardt and J. Bedo, 2007. Supervised feature selection via dependence estimation. Proceedings of the 24th International Conference on Machine Learning, June 20-24, 2007, Corvallis, OR., pp: 823-830.

Ulfenborg, B., K.K. Levan and B. Olsson, 2013. Classification of tumor samples from expression data using decision trunks. Cancer Inf., 12: 53-66.
CrossRef  |  PubMed  |  Direct Link  |  

Wach, S., E. Nolte, A. Theil, C. Stohr and T.T. Rau et al., 2013. MicroRNA profiles classify papillary renal cell carcinoma subtypes. Br. J. Cancer, 109: 714-722.
CrossRef  |  Direct Link  |  

Weston, J., A. Elisseeff, B. Scholkopf and M. Tipping, 2003. Use of the zero norm with linear models and kernel methods. J. Mach. Learn. Res., 3: 1439-1461.
Direct Link  |  

Xie, J. and C. Wang, 2011. Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases. Ex. Syst. Appl., 38: 5809-5815.
CrossRef  |  Direct Link  |  

Xu, R., J. Xu and D.C. Wunsch, 2009. MicroRNA expression profile based cancer classification using default ARTMAP. Neural Netw., 22: 774-780.
CrossRef  |  PubMed  |  Direct Link  |  

Xu, Z., R. Jin, J. Ye, M. Lyu and I. King, 2010. Discriminative semi-supervised feature selection via manifold regularization. Proceedings of the 21th International Joint Conference on Artificial Intelligence, June 21-July 8, 2010, Germany, pp: 1033-1047.

Yu, L. and H. Liu, 2003. Feature selection for high-dimensional data: A fast correlation-based filter solution. Proceedings of the International Conference on Machine Learning, August 21-24, 2003, ACM, Washington, DC., USA., ISBN:1577351894, pp: 856-863.

Zhao, Z. and H. Liu, 2007. Semi-Supervised Feature Selection Via Spectral Analysis. In: Proceedings of the SIAM International Conference on Data Mining, Apte, C.V. (Ed.). Society for Industrial and Applied Mathematics Publisher, Philadelphia, Pennsylvania, ISBN:9780898716306, pp: 641-646.