Data Analytics in Bioinformatics. Группа авторов

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      35. Cangelosi, D., Pelassa, S., Morini, M., Conte, M., Bosco, M.C., Eva, A., Sementa, A.R., Varesio, L., Artificial neural network classifier predicts neuroblastoma patients’ outcome. BMC Bioinf., 17, 12, 347, 2016.

      36. Nayeem, Md O. G., Ning Wan, M., Hasan, Md K., Prediction of disease level using multilayer perceptron of artificial neural network for patient monitoring. Int. J. Soft Comput. Eng. (IJSCE), 5, 7−23, 2015.

      37. Bordoloi, H. and Sarma, K.K., Protein structure prediction using multiple artificial neural network classifier, in: Soft Computing Techniques in Vision Science, pp. 137–146, Springer, Berlin, Heidelberg, 2012.

      38. Shanthi, D., Sahoo, G., Saravanan, N., Input Feature Selection using Hybrid Neuro-Genetic Approach in the diagnosis of Stroke. Int. J. Comput. Sci. Netw. Secur., 8, 12, 2008.

      39. Rahman, Md A., Chandren Muniyandi, R., Islam, Kh T., Rahman, Md M., Ovarian Cancer Classification Accuracy Analysis Using 15-Neuron Artificial Neural Networks Model, in: 2019 IEEE Student Conference on Research and Development (SCOReD), IEEE, pp. 33–38, 2019.

      40. Ghani, M.U., Alam, T.M., Jaskani, F.H., Comparison of Classification Models for Early Prediction of Breast Cancer, in: 2019 International Conference on Innovative Computing (ICIC), IEEE, pp. 1–6, 2019.

      41. Song, X., Mitnitski, A., Cox, J., Rockwood, K., Comparison of machine learning techniques with classical statistical models in predicting health outcomes, in: Medinfo, pp. 736–740, 2004.

      42. Tiwari, M., Bharuka, R., Shah, P., Lokare, R., Breast Cancer Prediction Using Deep Learning and Machine Learning Techniques, Available at SSRN 3558786, 2020.

      43. Kaur, R. and Ginige, J.A., Comparative Evaluation of Accuracy of Selected Machine Learning Classification Techniques for Diagnosis of Cancer: A Data Mining Approach. Int. J. Biomed. Biol. Eng., 12, 19–25, 2018.

      44. Mello-Román, J.D., Mello-Román, J.C., Gomez-Guerrero, S., García-Torres, M., Predictive Models for the Medical Diagnosis of Dengue: A Case Study in Paraguay, in: Computational and Mathematical Methods in Medicine, 2019.

      45. Subhadra, K. and Vikas, B., Neural network based intelligent system for predicting heart disease. Int. J. Innov. Technol. Exploring Eng. (IJITEE), 8, 5, 484–487, 2019.

      46. Dwivedi, A.K., Artificial neural network model for effective cancer classification using microarray gene expression data. Neural Comput. Appl., 29, 12, 1545–1554, 2018.

      47. Sethi, L., Jha, A.K., Chandrakar, H.K., A novel PSO-MLP framework for feature selection and classification of gene expression data. Yeast, 1484, 17, 2013.

      48. Padmavati, J., A comparative study on breast cancer prediction using RBF and MLP. Int. J. Sci. Eng. Res., 2, 1, 1–5, 2011.

      49. Pellakuri, V., Rajeswara Rao, D., Murthy, J.V.R., Modeling of supervised ADALINE neural network learning technique, in: 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), IEEE, pp. 17–22, 2016.

      50. Sonawane, J.S. and Patil, D.R., Prediction of heart disease using multilayer perceptron neural network, in: International Conference on Information Communication and Embedded Systems (ICICES2014), IEEE, pp. 1–6, 2014.

      51. Öz, E. and Kaya, H., Support vector machines for quality control of DNA sequencing. J. Inequal. Appl., 1, 2013, 85, 2013.

      *Corresponding author: [email protected]

Part 2 MACHINE LEARNING AND GENOMIC TECHNOLOGY, FEATURE SELECTION AND DIMENSIONALITY REDUCTION