Investigando a relação entre os aminoácidos de proteínas do vírus da dengue e o desfecho clínico do paciente
Abstract
In this work we propose a simplified method to represent dengue virus proteins and classify them according to the severity of the infection, which are classic and severe. These classes identify the clinical outcome of the patient, allowing to link the genomic composition of the virus and the reaction it caused in patients. To accomplish this, we transformed the protein sequences into a set of complex networks (graphs), from which histograms with the degree of nodes were generated. The representations were classified by a Decision Tree. For validation, the Leave-One-Out method was used. The classifier reached an AUC between 70% to 84%.
Keywords:
Bioinformática, Vírus da dengue, Redes Complexas
References
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Bland, J.M., Altman, D.G. (1996). “Statistics notes: measurement error”. BMJ. 312 (7047): 1654. doi:10.1136/bmj.312.7047.1654. PMC 2351401. PMID 8664723.
Diestel, R. (2005). “Graph Theory” 3ª ed. Berlin, New York: Springer-Verlag. ISBN 978-3-540-26183-4.
Foster, D.V., Foster, J.G.,Grassberger, P., Paczuski, M. (2011). “Clustering drives assortativity and community structure in ensembles of networks”. Physical review. E, Statistical, nonlinear, and soft matter physics. 84 (6 Pt 2). 066117 páginas. PMID 22304165. doi:10.1103/PhysRevE.84.066117.
Freeman, L. (1977). "A set of measures of centrality based on betweenness". Sociometry. 40(1): 35–41. doi:10.2307/3033543. JSTOR 3033543.
Holland, P.W. and Leinhardt, S. (1971). "Transitivity in structural models of small groups". Comparative Group Studies 2: 107–124.
Ito, E.A., Katahira, I., Vicente, F.F.R., Pereira, L.F.P., and Lopes, F.M. (2017). “BASiNET––BiologicAl Sequences NETwork: a case study on coding and non-coding RNAs identification”. Nucleic Acids Research, 2018, Vol. 46, No. 16.
Iqbal N. and Islam M. (2019). “Machine Learning for Dengue Outbreak Prediction: A Performance Evaluation of Different Prominent Classifiers”. Informatica 43 (2019) 363–371. https://doi.org/10.31449/inf.v43i1.1548
Mao, G. and Zhang, N. (2013) “Analysis of Average Shortest-Path Length of Scale-Free Network”. doi: 10.1155/2013/865643.
Metz, J., Calvo, R., Seno, E.R.M., Romero, R.A.F., Liang, Z. (2007). “Redes Complexas: conceitos e aplicações”. Instituto de Ciências Matemáticas e de Computação, No 290, São Carlos, 2007, páginas 9--21, issn: 0103-2569
Sani, H.M., Lei, C., Neagu, D. (2018). “Computational Complexity Analysis of Decision Tree Algorithms”. In: Bramer, M., Petridis, M. (eds) Artificial Intelligence XXXV. SGAI 2018. Lecture Notes in Computer Science, vol 11311. Springer, Cham.
Souza, L.R., Colonna, J.G., Comodaro, J.M. (2022) “Using amino acids co-occurrence matrices and explainability model to investigate patterns in dengue virus proteins”. BMC Bioinformatics 23, 80. https://doi.org/10.1186/s12859-022-04597-y
Published
2022-06-07
How to Cite
QUEIROZ, Diego; CUNHA, Fagner; SOUZA, Leonardo Rodrigues; COLONNA, Juan G..
Investigando a relação entre os aminoácidos de proteínas do vírus da dengue e o desfecho clínico do paciente. In: WORK IN PROGRESS - BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTHCARE (SBCAS), 22. , 2022, Teresina/PI.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 92-97.
ISSN 2763-8987.
DOI: https://doi.org/10.5753/sbcas_estendido.2022.222463.
