[1] Aggarwal, C.C., 2018. Neural Networks and Deep Learning. Springer. doi: 10.1007/978-3-319-94463-0
[2] Banerjee, S. and Sarkar, R.R., 2008. Delay-induced model for tumor-immune interaction and control of malignant tumor growth. Biosystems, 91(1), pp.268-288. doi:10.1016/j.biosystems.2007.10.002
[3] Bekisz, S. and Geris, L., 2020. Cancer modelling: from mechanistic to data-driven approaches, and from fundamental insights to clinical applications. Journal of Computational Science, 46, pp.101198.doi: 10.1016/j.jocs.2020.101198
[4] Borges, F., Iarosz, K., Ren, H., Batista, A., Baptista, M., Viana, R., Lopes, S. and Grebogi, C., 2014.
Model for tumour growth with treatment by continuous and pulsed chemotherapy. Biosystems, 116,
pp.43-48. doi: 10.1016/j.biosystems.2013.12.001
[5] Franssen, L.C., Lorenzi, T., Burgess, A.E. and Chaplain, M.A., 2019. A mathematical framework for modelling the metastatic spread of cancer. Bulletin of Mathematical Biology, 81(6), pp.1965-2010. doi:10.1007/s11538-019-00597-x
[6] Géron, A., 2022. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, O’Reilly
Media. Inc..
[7] Gestel, T.V., Suykens, J.A.K., Baesens, B., Viaene, S., Vanthienen, J., Dedene, G., Moor, B.D. and Vandewalle, J., 2004. Benchmarking least squares support vector machine classifiers. Machine Learning, 54, pp.5-32. doi: 10.1023/B:MACH.0000008082.80494.e0
[8] Greene, J.M., Gevertz, J.N. and Sontag, E.D., 2019. Mathematical approach to differetiate spontaneous and induced evolution to drug resistance durig cancer treatment. JCO Clinical Cancer Informatics, 3,pp.1-20. doi: 10.1200/CCI.18.00087
[9] Mehrkanoon, S., Falck, T. and Suykens, A.K., 2012. Approximate solutions to ordinary differential equations using least squares support vector machiens. IEEE Transactions on Neural Networks and Learning Systems, 23(9), pp.1356-1367. doi: 10.1109/TNNLS.2012.2202126
[10] Mehrkanoon, S. and Suykens, J.A.K., 2015. Learning solution to partial differential equations using LS-SVM. Neurocomputing, 159, pp.105-116. dio: 10.1016/j.neucom.2015.02.013
[11] Mohri, M., Rostamizadeh, A. and Talwalkar, A., 2018. Foundations of Machine Learning. MIT Press.
[12] Padmanabhan, R., Meskin, N. and Ala-Eddin, A.M., 2021. Mathematical Models of Cancer and
Different Therapies. Springer: Singapore. dio: 10.1007/978-981-15-8640-8
[13] Pakniyat, A., Parand, K. and Jani, M., 2021. Least squares support vector regression for differential equations on unbounded domains. Chaos Solitions and Fractals, 151, pp.111-232. doi:
10.1016/j.chaos.2021.111232
[14] Parand, K., Aghaei, A.A., Jani, M. and Ghodsi, A., 2021. A new approach to the numerical solution of Fredholm intergal equations using least squares-support vector regression. Mathematics and Computers in Simulation, 180, pp.114-128. doi: 10.1016/j.matcom.2020.08.010
[15] Pinho, S., Freedman, H. and Nani, F., 2002. A chemotherapy model for the treatment of cancer with metastasis. Mathematical and Computer Modelling, 36(7-8), pp.773-803. doi: 10.1016/S0895-7177(02)00227-3
[16] Pillis, L.D. and Radunskaya, A., 2001. A mathematical tumor model with immune resistance and drug therapy: an optimal control approach. Computational and Mathematical Methods in Medicine,3(2), pp.79-100. doi: 10.1080/10273660108833067
[17] Pillis, L.D. and Radunskaya, A., 2003. The dynamics of an optimally controlled tumor model: a case study. Mathematical and Computer Modelling, 37(11), pp.1221-1244. doi: 10.1016/S0895- 7177(03)00133-X
[18] Sun, X. and Hu, B., 2018. Mathematical modeling and computational prediction of cancer drug resistance. Briefings in Bioinformatics, 19(6), pp.1382-1399. doi: 10.1093/bib/bbx065
[19] Suykens, A.K., Gestel, T.V., Brabanter, J.D., Moor, B.D. and Vandewalle, J., 2002. Least-Squares Support Vector Machines. World Scientific.
[20] Schölkopf, B., Luo, Zh. and Vovk, V., 2013. Empirical Inference: Festchrifl in Honor of Vladimir N. Vapnik. Springer. doi: 10.1007/978-3-642-41136-6
[21] Tse, S.M., Liang, Y., Leung, K.S., Lee, K.H. and Mok, T.S.K., 2007. A memetic algorithm for
multiple-drug cancer chemotherapy schedule optimization. IEEE Transactions on Systems, Man, and Cybernetics. Part B , 37(1), pp.84-91. doi: 10.1109/TSMCB.2006.883265
[22] Wu, H.K., Chen, P.J. and Hsieh, J.G., 2006. Simple algorithms for least square support vector
machines. IEEE International Conference on Systems; Man and Cybernetics, 6, pp.5106-5111. doi:
10.1109/ICSMC.2006.385118
)