References

MACHINE LEARNING METHOD TO DIFFERENTIATE ATAXIAS


[1] H. L. Paulson, The spinocerebellar ataxias, Journal of Neuro-Ophthalmology: The Official Journal of the North American Neuro-Ophthalmology Society 29(3) (2009), 227-237.
DOI: https://doi.org/10.1097/WNO0b013e3181b416de

[2] J. Lim, T. Hao, C. Shaw, A. J. Patel, G. Szabó, J.-F. Rual, C. J. Fisk, N. Li, A. Smolyar, D. E. Hill, A.-L. Barabási, M. Vidal and H. Y. Zoghbi, A protein-protein interaction network for human inherited ataxias and disorders of Purkinje cell degeneration, Cell 125(4) (2006), 801-814.
DOI: https://doi.org/10.1016/j.cell.2006.03.032

[3] G. S. Carnivali and S. V. A. Campos, Does the ataxia group have genetic similarities?, Anais do XIII Encontro Academico de Modelagem Computacional (2020), 135.

[4] B. Snel, G. Lehmann, P. Bork and M. A. Huynen, STRING: A web-server toretrieve and display the repeatedly occurring neighbourhood of a gene, Nucleic Acids Research 28(18) (2000), 3442-3444.
DOI: https://doi.org/10.1093/nar/28.18.3442

[5] M. De Souto, A. Lorena, A. Delbem and A. de Carvalho, Tecnicas de aprendizado de maquina para problemas de biologia molecular, Sociedade Brasileira de Computacao 1(2) (2003).

[6] M. Fatima and M. Pasha, Survey of machine learning algorithms for disease diagnostic, Journal of Intelligent Learning Systems and Applications 9(1) (2017), 1-16.
DOI: https://doi.org/10.4236/jilsa.2017.91001

[7] D. Szklarczyk, A. Franceschini, S. Wyder, K. Forslund, D. Heller, J. Huerta-Cepas, M. Simonovic, A. Roth, A. Santos, K. P. Tsafou, M. Kuhn, P. Bork, L. J. Jensen and C. Von Mering, STRING v10: protein–protein interaction networks, integrated over the tree of life, Nucleic Acids Research 43(D1) (2015), 447-452.
DOI: https://doi.org/10.1093/nar/gku1003

[8] A. Brazma and J. Vilo, Gene expression data analysis, FEBS Letters 480(1) (2000), 17-24.
DOI: https://doi.org/10.1016/s0014-5793(00)01772-5

[9] S. Horvath and J. Dong, Geometric interpretation of gene coexpression network analysis, PLoS Computational Biology 4(8) (2008); e1000117.
DOI: https://doi.org/10.1371/journal.pcbi.1000117

[10] G. Goeckenjan, H. Sitter, M. Thomas, D. Branscheid, M. Flentje, F. Griesinger, N. Niederle, M. Stuschke, T. Blum, K.-M. Deppermann, J. H. Ficker, L. Freitag, A. S. Lübbe, T. Reinhold, E. Späth-Schwalbe, D. Ukena, M. Wickert, M. Wolf, S. Andreas, T. Auberger, R. P. Baum, B. Baysal, J. Beuth, H. Bickeböller, A. Böcking, R. M. Bohle, I. Brüske, O. Burghuber, N. Dickgreber, S. Diederich, H. Dienemann, W. Eberhardt, S. Eggeling, T. Fink, B. Fischer, M. Franke, G. Friedel, T. Gauler, S. Gütz, H. Hautmann, A. Hellmann, D. Hellwig, F. Herth, C. P. Heußel, W. Hilbe, F. Hoffmeyer, M. Horneber, R. M. Huber, J. Hübner, H.-U. Kauczor, K. Kirchbacher, D. Kirsten, T. Kraus, S. M. Lang, U. Martens, A. Mohn-Staudner, K.-M. Müller, J. Müller-Nordhorn, D. Nowak, U. Ochmann, B. Passlick, I. Petersen, R. Pirker, B. Pokrajac, M. Reck, S. Riha, C. Rübe, A. Schmittel, N. Schönfeld, W. Schütte, M. Serke, G. Stamatis, M. Steingräber, M. Steins, E. Stoelben, L. Swoboda, H. Teschler, H. W. Tessen, M. Weber, A. Werner, H.-E. Wichmann, E. Irlinger Wimmer, C. Witt and H. Worth, Prävention, Diagnostik, Therapie und Nachsorge des Lungenkarzinoms, Pubmed Results, Pneumologie 65(8) (2011), e51-e75.
DOI: https://doi.org/10.1055/s-0030-1256562

[11] T. Pimentel, A. Veloso and N. Ziviani, Fast node embeddings: Learning egocentric representations, 2018.

[12] M. C. Monard and J. A. Baranauskas, Conceitos sobre aprendizado de maquina, Sistemas Inteligentes: Fundamentos e Aplicacoes 1(1) (2003), 32.

[13] J. Gama, Arvores de decisao, Palestra ministrada no Nucleo da Ciencia de Computacao da Universidade do Porto, Porto, 2002.

[14] G. Keijzers, D. Bakula and M. Scheibye-Knudsen, Monogenic diseases of DNA repair, New England Journal of Medicine 377(19) (2017), 1868-1876.
DOI: https://doi.org/10.1056/NEJMra1703366

[15] J. M. Stuart, E. Segal, D. Koller and S. K. Kim, A gene-coexpression network for global discovery of conserved genetic modules, Science 302(5643) (2003), 249-255.
DOI: https://doi.org/10.1126/science.1087447

[16] A. Dagliati, S. Marini, L. Sacchi, G. Cogni, M. Teliti, V. Tibollo, Pasquale de Cata, Luca Chiovato, and Riccardo Bellazzi, Machine learning methods to predict diabetes complications, Journal of Diabetes Science and Technology 12(2) (2018), 295-302.
DOI: https://doi.org/10.1177/1932296817706375

[17] S. Uddin, A. Khan, M. E. Hossain and M. A. Moni, Comparing different supervised machine learning algorithms for disease prediction, BMC Medical Informatics and Decision Making 19(1) (2019), 1-16.
DOI: https://doi.org/10.1186/s12911-019-1004-8

[18] S. Pouriyeh, S. Vahid, G. Sannino, G. De Pietro, H. Arabnia and J. Gutierrez, A comprehensive investigation and comparison of machine learning techniques in the domain of heart disease, In 2017 IEEE Symposium on Computers and Communications (ISCC) (2017), pp. 204-207.
DOI: https://doi.org/10.1109/ISCC.2017.8024530

[19] M. Brahimi, K. Boukhalfa and A. Moussaoui, Deep learning for tomato diseases: Classification and symptoms visualization, Applied Artificial Intelligence 31(4) (2017), 299-315.
DOI: https://doi.org/10.1080/08839514.2017.1315516