Data Analytics in Bioinformatics. Группа авторов
Чтение книги онлайн.
Читать онлайн книгу Data Analytics in Bioinformatics - Группа авторов страница 32
![Data Analytics in Bioinformatics - Группа авторов Data Analytics in Bioinformatics - Группа авторов](/cover_pre859791.jpg)
8. https://www.analyticssteps.com/blogs/understanding-bioinformatics-application-machine-learning.
9. Lancashire, L.J., Lemetre, C., Ball, G.R., An introduction to artificial neural networks in bioinformatics—Application to complex microarray and mass spectrometry datasets in cancer studies. Briefings Bioinf., 10, 3, 315–329, 2009.
10. https://microbenotes.com/biological-databases-types-and-importance/.
11. https://ceoworld.biz/2019/12/13/data-preprocessing-what-is-it-and-why-is-important/.
12. Herbert, K.G. and Wang, J.T.L., Biological data cleaning: A case study. Int. J. Inf. Qual., 1, 1, 60–82, 2007.
13. Chen, Q., Duplication in biological databases: definitions, impacts and methods (Doctoral dissertation), University of Melbourne, Australia, 2017.
14. Lee, G., Rodriguez, C., Madabhushi, A., An empirical comparison of dimensionality reduction methods for classifying gene and protein expression datasets, in: International Symposium on Bioinformatics Research and Applications, Springer, Berlin, Heidelberg, pp. 170–181, 2007.
15. Lausen, B., Bioinformatics and classification: The analysis of genome expression data, in: Classification, Clustering, and Data Analysis, pp. 455–461, Springer, Berlin, Heidelberg, 2002.
17. https://www.kdnuggets.com/2018/10/simple-neural-network-python.html.
18. https://www.tutorialspoint.com/artificial_neural_network/artificial_neural_network_supervised_learning.html.19. https://www.mygreatlearning.com/blog/types-of-neural-networks/.
21. Boutros, P.C. and Okey, A.B., Unsupervised pattern recognition: An introduction to the whys and wherefores of clustering microarray data. Briefings Bioinf., 6, 4, 331–343, 2005.
22. Wang, J.-Y., Application of support vector machines in bioinformatics, National Taiwan University, Taiwan, 2002. 23. https://towardsdatascience.com/logistic-regression-classifier-8583e0c3cf9.
24. Polaka, I., Tom, I., Borisov, A., Decision tree classifiers in bioinformatics. Appl. Comput. Syst., 42, 1, 118–123, 2010. 25. https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761.
26. Qi, Y., Random forest for bioinformatics, in: Ensemble machine learning, pp. 307–323, Springer, Boston, MA, 2012.
27. Samundeeswari, E. and Saranya, P., An artificial neural network model for prediction of survival time of breast cancer dataset. Int. J. Res. Eng. Appl. Sci., 6, 1, 161–168, 2016.
28. Narayanan, A., Keedwell, E.C., Gamalielsson, J., Tatineni, S., Single-layer artificial neural networks for gene expression analysis. Neurocomputing, 61, 217–240, 2004.
29. Hu, Y., Ashenayi, K., Veltri, R., O’Dowd, G., Miller, G., Hurst, R., Bonner, R., A comparison of neural network and fuzzy c-means methods in bladder cancer cell classification, in: Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN’94), vol. 6, IEEE, pp. 3461–3466, 1994.
30. Won, H.-H. and Cho, S.-B., Paired neural network with negatively correlated features for cancer classification in DNA gene expression profiles, in: Proceedings of the International Joint Conference on Neural Networks, 2003, vol. 3, pp. 1708–1713, IEEE, Portland, Oregon, 2003.
31. Thein, H.T.T. and Tun, K.M.M., An approach for breast cancer diagnosis classification using neural network. Adv. Comput., 6, 1, 1, 2015.
32. Peterson, L.E. and Coleman, M.A., Machine learning-based receiver operating characteristic (ROC) curves for crisp and fuzzy classification of DNA microarrays in cancer research. Int. J. Approximate Reasoning, 47, 1, 17–36, 2008.
33. Tabares-Soto, R., Orozco-Arias, S., Romero-Cano, V., Segovia Bucheli, V., Rodríguez-Sotelo, J.L., Jiménez-Varón, C.F., A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data. PeerJ Comput. Sci., 6, e270, 2020.
34. Wei, J.S., Greer, B.T., Westermann, F., Steinberg, S.M., Son, C.-G., Chen, Q.-R., Whiteford, C.C. et al., Prediction of clinical outcome using gene expression profiling and artificial neural networks for patients with neuroblastoma. Cancer Res., 64, 19, 6883–6891, 2004.
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