[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