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a) É possível montar um conjunto de amostras negativas, a partir das amostras positivas, com maior grau de certeza que abordagens de montagem de par aleatório;

b) O modelo construído é capaz de predizer interações interespécies entre proteínas do ZIKV e de seus hospedeiros, baseado nas características físico- químicas das suas proteínas;

c) A abordagem do Random Forrest consegue uma taxa de erro semelhantes à de outras abordagens encontradas na literatura, porém aplicado à vários hospedeiros;

d) Os dados apontam que as proteínas NS1 e NS5 são as que mais fazem interações na maioria das espécies analisadas;

e) A topologia das redes de interações preditas pelo modelo pode ser representada através de grafos bipartidos;

f) A análise de enriquecimento de dados mostra que há a possibilidade dos mesmos efeitos no desenvolvimento do sistema nervoso observada em H.

REFERÊNCIAS

ADAMS, P. D. et al. Cross-validated maximum likelihood enhances crystallographic simulated annealing refinement. Proc. Natl. Acad. Sci. U.S.A., Washington, v.10, p. 5018–5023, 1997.

AMORIM, L. B. et al. Susceptibility status of Culex quinquefasciatus (Diptera: Culicidae) populations to the chemical insecticide temephos in Pernambuco, Brazil. Pest manag. sci., West Sussex, v. 69, n. 12, p. 1307–1314, 2013.

ARAUJO, T. V. B. et al. Association between microcephaly, Zika virus infection, and other risk factors in Brazil: Final report of a case-control study. Lancet Infect. Dis., London, v.18, n.3, p. 328-336, 2017.

AYRES, C. F. J. Identification of Zika virus vectors and implications for control. The Lancet Infect. Dis., London, v. 16, n. 3, p. 278–279, 2016.

BEARCROFT, W. G. C. Zika Virus Infection Experimentally Induced in a Human Volunteer. Trans. R. Soc. Trop. Med. Hyg., London, v. 50, n. 5, p. 442–448, 1956. BEN-HUR, A.; NOBLE, W. S. Kernel methods for predicting protein – protein

interactions. Bioinformatics, Oxford, v. 21, p. i38–i46, 2005.

BOORMAN, J. P. T.; PORTERFIELD, J. S. A simple technique for infection of

mosquitoes with viruses transmission of Zika virus. Trans. R. Soc. Trop. Med. Hyg., London, v. 50, n. 3, p. 238–242,1956.

BREIMAN, L. Randomforest. Mach. Learn. Berkeley, v.45, n.1, p. 1–33, 2001. BUENO, M. G. et al. Animals in the Zika Virus Life Cycle: What to Expect from Megadiverse Latin American Countries. Plos Negl. Trop. Dis., San Francisco, v.10, 2016. Disponível em:

<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5179043/pdf/pntd.0005073.pdf>. Acesso em: 5 jun. 2017

CALDERONE, A.; LICATA, L.; CESARENI, G. VirusMentha: a new resource for virus-host protein interactions. Nucleic acids res., London, v. 43, p. D588–D592, 2015.

CAMACHO, C. et al. BLAST plus: architecture and applications. BMC Bioinformatics, London, v. 10, 2009. Disponível em:

<https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-10-421>. Acesso em: 5 jul. 2018

CAMPOS, T. D. L. et al. Revisiting Key Entry Routes of Human Epidemic

Arboviruses into the Mainland Americas through Large-Scale Phylogenomics. Int. J. Genomics, Cairo, 2018. Disponível em:

CARVALHO, R. G.; LOURENÇO-DE-OLIVEIRA, R.; BRAGA, I. A. Updating the geographical distribution and frequency of Aedes albopictus in Brazil with remarks regarding its range in the Americas. Mem. Inst. Oswaldo Cruz, Rio de Janeiro, v. 109, n. 6, p. 787–796, 2014.

CAUCHEMEZ, S. et al. Association between Zika virus and microcephaly in French Polynesia, 2013-15: A retrospective study. Lancet, London, v. 387, p. 2125–2132, 2016.

CHOUIN-CARNEIRO, T. et al. Differential Susceptibilities of Aedes aegypti and Aedes albopictus from the Americas to Zika Virus. Plos negl. trop. dis., San Francisco, v. 10, n. 3, 2016. Disponível em:

<https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0004543 >. Acesso em: 5 jun. 2017

CUI, G.; FANG, C.; HAN, K. Prediction of protein-protein interactions between viruses and human by an SVM model. BMC Bioinformatics, London, v. 13, 2012. Disponível em: <www.bmcbioinformatics.biomedcentral.com/articles/10.1186/1471- 2105-13-S7-S5 >. Acesso em: 15 jun. 2018

DIAGNE, C. T. et al. Potential of selected Senegalese Aedes spp. mosquitoes (Diptera: Culicidae) to transmit Zika virus. BMC Infect Dis, London, v. 15, 2015. Disponível em: < https://bmcinfectdis.biomedcentral.com/track/pdf/10.1186/s12879- 015-1231-2>. Acesso em: 18 jun. 2018

DICK, G. W. .; KITCHEN, S. .; HADDOW, A. . Zika Virus (I). Isolations and

serological specificity. Trans. R. Soc. Trop. Med. Hyg., London, v. 46, n. 5, p. 509– 520,1952.

DUFFY, M. R. et al. Zika virus outbreak on Yap Island, Federated States of Micronesia. New England J. of Med., Waltham, v. 360, p. 2536–2543, 2009. DYE, C. The analysis of parasite transmission by bloodsucking insects. Annu. Rev. Entomol, Palo Alto, v. 37, p. 1–19, 1992.

DYER, M. D.; MURALI, T. M.; SOBRAL, B. W. Computational prediction of host- pathogen protein-protein interactions. Bioinformatics, Oxford, v. 23, p. 159–166, 2007.

EID, F.-E.; ELHEFNAWI, M.; HEATH, L. S. DeNovo: virus-host sequence-based protein–protein interaction prediction. Bioinformatics, Oxford, v. 32, p 1144-1150, 2015.

ESTEVES, E. et al. New Targets for Zika Virus Determined by Human-Viral Interactomic: A Bioinformatics Approach. BioMed Res. Int., Cairo, v. 2017, 2017. Disponível em: <https://www.hindawi.com/journals/bmri/2017/1734151>. Acesso em: 2 ago. 2017

FAYE, O. et al. Molecular Evolution of Zika Virus during Its Emergence in the 20 th Century. Plos negl. trop. dis., San Francisco, v. 8, n. 1, 2014. Disponível em: <

https://journals.plos.org/plosntds/>. Acesso em: 15 Jul. 2018.

FERNANDES, R. S. et al. Culex quinquefasciatus from Rio de Janeiro Is Not

Competent to Transmit the Local Zika Virus. Plos negl. trop. dis., San Francisco, v. 10, n. 9, 2016. Disponível em:

<https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0004993>. Acesso em: 25 jun. 2017

FONTES-GARFIAS, C. R. et al. Functional analysis of glycosylation of Zika virus envelope protein. Cell Rep., Cambridge, v. 21, n. 5, p. 1180–1190, 2017.

FORATTINI, O. P. Principais mosquitos de importância sanitária no Brasil. Rio de Janeiro: Ed. Fiocruz, 1994.

FRANKEL, M. B. et al. Development of the Abbott RealTime ZIKA assay for the qualitative detection of Zika virus RNA from serum, plasma, urine, and whole blood specimens using the m2000 system. J. virol. methods, Amsterdam, v. 246, p. 117– 124, 2017.

GIRALDO-CALDERON, G. I. et al. VectorBase: An updated Bioinformatics Resource for invertebrate vectors and other organisms related with human diseases. Nucleic acids res., London, v. 43, n. D1, p. D707–D713, 2015.

GUEDES, D. R. et al. Zika virus replication in the mosquito Culex quinquefasciatus in Brazil. Emerg. Microbes Infect., New York, v. 6, p. 1-11, 2017. Disponível em: <http://biorxiv.org/content/biorxiv/early/2016/09/02/073197.full.pdf>. Acesso em: 5 ago. 2017.

GUO, X.-X. et al. Culex pipiens quinquefasciatus: a potential vector to transmit Zika virus. Emerg. Microbes Infect., New York, v. 5, n. 9, p. e102, 2016. Disponível em: <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5113053/pdf/emi2016102a.pdf>. Acesso em: 6 jul. 2017.

GUO, Y. et al. Using support vector machine combined with auto covariance to predict protein-protein interactions from protein sequences. Nucleic acids res., London, v. 36, n. 9, p. 3025–3030, 2008.

GURUMAYUM, S. et al. ZikaBase: An integrated ZIKV- Human Interactome Map database. Virology, New York, v. 514, p. 203–210, 2018.

HARRINGTON, E. D.; JENSEN, L. J.; BORK, P. Predicting biological networks from genomic data. FEBS letters, Amsterdam, v. 582, n. 8, p. 1251–8, 2008.

HSU, C-W., CHANG, C-C., LIN, C-J. A Practical Guide to Support Vector Classification. Taipei, 2008. Disponível em:

<www.ic.unicamp.br/~wainer/cursos/2s2008/ia/guide-svm.pdf>. Acesso em: 15 jun. 2017.

HUANG, Q. et al. Prediction of protein–protein interactions with clustered amino acids and weighted sparse representation. Int. J. Mol. Sci., Basel, v. 16, n. 5, p.

10855–10869, 2015.

HUANG, Y-J. S. et al. Flavivirus-Mosquito Interactions. Viruses, Basel, v. 6, n.11, p. 4703–4730, 2014.

JANSEN, R.; GERSTEIN, M. Analyzing protein function on a genomic scale: the importance of gold-standard positives and negatives for network prediction. Curr. opin. microbiol., New York, v. 7, n. 5, p. 535–545, 2004.

JAYANTHI, S. K.; PREMA, S. Segregating unique service object from multi-web sources for effective visualization" In: INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, INFORMATICS AND MEDICAL ENGINEERING, 3., 2012, Salem. Annals. New York: IEEE, 2012. p. 30-35. Disponível em:

<http://ieeexplore.ieee.org/document/5942039/>. Acesso em: 6 jun. 2017.

JIAO, X. et al. Databases and ontologies DAVID-WS: a stateful web service to facilitate gene/protein list analysis. Bioinformatics, Oxford, v. 28, n. 13, p. 1805– 1806, 2012.

KOYABU, S.; PHAN, T.T.T.; OHKAWA, T. Extraction of Protein-Protein Interaction from Scientific Articles by Predicting Dominant Keywords. BioMed Res. Int., Cairo, v. 2015, 2015. Disponível em: <https://www.hindawi.com/journals/bmri/2015/928531> Acesso em: 26 jul. 2017.

KRAEMER, M. U. et al. The global compendium of Aedes aegypti and Ae. albopictus occurrence. Sci. data, Londron, v. 2, 2015. Disponível em:

<https://www.nature.com/articles/sdata201535.pdf>. Acesso em: 25 jul. 2017.

KUHN, M. Predictive Modeling with R and the caret Package. 2013. Disponível em: < https://www.r-project.org/conferences/useR-

2013/Tutorials/kuhn/user_caret_2up.pdf>. Acesso em: 27 jul. 2017.

KUNO, G.; CHANG, G. J. J. Full-length sequencing and genomic characterization of Bagaza, Kedougou, and Zika viruses. Arch. virol., Wien, v. 152, n. 4, p. 687–696, 2007.

LEES, J. G. et al. Systematic computational prediction of protein interaction networks. Phys. biol., Bristol, v. 8, n. 3, 2011.

LI, M.I. et al. Oral Susceptibility of Singapore Aedes (Stegomyia) aegypti (Linnaeus) to Zika Virus. Plos negl. trop. dis., San Francisco, v. 6, n. 8, p. e1792, 2012. Disponível em:

<https://journals.plos.org/plosntds/article?id=10.1371/journal.pntd.0001792>. Acesso em: 5 ago. 2017

LIANG, Q. et al. Zika Virus NS4A and NS4B Proteins Deregulate Akt-mTOR Signaling in Human Fetal Neural Stem Cells to Inhibit Neurogenesis and Induce Autophagy. Cell Stem Cell, Cambridge, v. 19, n. 5, p. 663–671, 2016.

LIU, G. H.; SHEN, H. BIN; YU, D. J. Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures. J. Membr. Biol., New York, v.249, n.1-2, p. 141–153, 2015.

LOUPPE, G. Understanding Random Forests: From Theory to Practice. 2014. 223 f. Dissertação (PhD em Electrical Engineering and Computer Science) - Montefiore Institute, University of Liège, Liège. 2015. Disponível em:

<https://arxiv.org/pdf/1407.7502.pdf>.Acesso em: 18 jan. 2018.

LOURIDAS, P.; EBERT, C. Machine Learning. IEEE Software, New York v. 33, n. 5, p. 110–115, 2016.

LOWE, R. et al. The zika virus epidemic in brazil: From discovery to future

implications. Int. J. Environ. Res. Public Health, Basel, v. 15, n. 1, p. 2-18, 2018. Disponível em: < https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5800195/> Acesso em: 5 set. 2017.

MANSFIELD, K. Marmoset models commonly used in biomedical research. Comp. med. Memphis, v. 53, n. 4, p. 383–392, 2003.

MARCONDES, C. B.; XIMENES, M. F. F. M. Zika virus in Brazil and the danger of infestation by Aedes (Stegomyia) mosquitoes. Rev. Soc. Bras. Med. Trop., Brasilia, v. 49, n. 1, p. 4–10, 2015.

MATTHEWS, L. R. et al. Identification of potential interaction networks using

sequence-based searches for conserved protein-protein interactions or "interologs". Genome Res., London, v. 11, n.12, p. 2120–2126, 2001.

MEYER, D. A. Support Vector Machines: The Interface to libsvm in package e1071. Wien: FH Technikum, 2001. Disponível em: <https://cran.r-

project.org/web/packages/e1071/vignettes/svmdoc.pdf> Acesso em: Acesso em: 2 ago. 2017.

MONTGOMERY, S. H.; MUNDY, N. I. Microcephaly genes evolved adaptively throughout the evolution of eutherian mammals. BMC Evol. Biol. London, v.120, 2014. Disponível em: <http://www.biomedcentral.com/1471-2148/14/120>. Acesso em: 30 ago. 2018.

MOORE, C. G. et al. Aedes albopictus in the United States: rapid spread of a potential disease vector. J. Am. Mosq. Control Assoc., Fresno, v. 4, n. 3, p. 356– 361, 1988.

NOURANI, E.; KHUNJUSH, F.; DURMUS, S. Computational approaches for prediction of pathogen-host protein-protein interactions. Front. Microbiol., Lausanne, v.6, 2015. Disponível em:

<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4338785/pdf/fmicb-06-00094.pdf>. Acesso em: 28 ago. 2017.

OEHLER, E. et al. Zika virus infection complicated by Guillain-Barre syndrome--case report, French Polynesia, December 2013. Euro Surveill., Stockholm, v. 19, n. 9, p. 7–9, 2014.

PAIXÃO, E. S. et al. History, epidemiology, and clinical manifestations of Zika: A systematic review. Am. J. Public. Health, New York, v. 106, n. 4, p. 606-612, 2016. Disponível

em:<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4816002/pdf/AJPH.2016.303112 .pdf>. Acesso em: 1 jun. 2017.

PAPANIKOLAOU, N. et al. Protein-protein interaction predictions using text mining methods. Methods, San Diego, v. 74, p. 47–53, 2015.

PAVITHRA L. CHAVALI, et al. Neurodevelopmental protein Musashi 1 interacts with the Zika genome and promotes viral replication. Science, New York, v.357, p.83-88, 2017.

PELLEGRINI, M. et al. Assigning protein functions by comparative genome analysis: protein phylogenetic profiles. Proc. Natl. Acad. Sci. U.S.A., Washington, v. 96, n. 8, p. 4285–4288, 1999.

PEREIRA-SILVA, J. W. et al. First evidence of zika virus venereal transmission in aedes aegypti mosquitoes. Mem. Inst. Oswaldo Cruz., Rio de Janeiro, v. 113, n. 1, p. 56–61, 2018.

PICKETT, B. E. et al. ViPR: An open bioinformatics database and analysis resource for virology research. Nucleic acids res., London, v. 40, n. D1, p. 593–598, 2012. POWELL, J. R.; TABACHNICK, W. J. History of domestication and spread of Aedes aegypti--a review. Mem. Inst. Oswaldo Cruz, Rio de Janeiro, v.108, p.11-17, 2013.

QI, Y.; KLEIN-SEETHARAMAN, J.; BAR-JOSEPH, Z. Random Forest Similarity for Protein-Protein Interaction Prediction from Multiple Sources. Pac. Symp.

Biocomput., Singapore, v. 10, p. 531–542, 2005.

RAMASUBRAMANIAN, K.; SINGH, A. Machine Learning Using R. 1 ed. Berkeley: Apress, 2017.

RICHARD, V.; PAOAAFAITE, T.; CAO-LORMEAU, V. M. Vector Competence of French Polynesian Aedes aegypti and Aedes polynesiensis for Zika Virus. Plos negl. trop. dis., San Francisco, v. 10, n. 9, 2016. Disponível em:

<http://dx.plos.org/10.1371/journal.pntd.0005024>. Acesso em: 2 jun. 2017.

ROST, B. Twilight zone of protein sequence alignments. Protein Eng., Oxford, v. 12, n. 2, p. 85–94, 1999.

SAIZ, J-C. et al. Zika virus: The latest newcomer. Front. Microbiol., Lausanne, v. 7, 2016. Disponível em:

Acesso em: 29 maio. 2017.

SAVAGE, H. M. et al. Epidemic of dengue-4 virus in Yap State, Federated States of Micronesia, and implication of Aedes hensilli as an epidemic vector. Trans. R. Soc. Trop. Med. Hyg., London, v. 58, n. 4, p. 519–524, 1998.

SHEN, J. et al. Predicting protein-protein interactions based only on sequences information. Proc. Natl. Acad. Sci. U.S.A., Washington, v. 104, n. 11, p. 4337–4341, 2007.

SILVA, G. S. DA; CRUZ, M. A. O. Comportamento e composiçao de um grupo de Callithrix jacchus Erxleben (Primates, Callithrichidae) na Mata de Dois Irmãos, Recife, Pernambuco, Brasil. Rev. Bras. Zool., São Paulo, v. 10, n. 3, p. 509–520, 1993.

SILVA, H. H.; SILVA, I. G. Influência do período de quiescência dos ovos sobre o ciclo de vida de Aedes aegypti (Linnaeus, 1762) (Diptera, Culicidae) em condições de laboratório. Rev. Soc. Bras. Med. Trop. Brasilia, v. 32, n. 4, p. 349–355, 1999. SINGHAL, M.; RESAT, H. A domain-based approach to predict protein-protein interactions. BMC Bioinformatics, London, v.199, n.8, 2007. Disponível em: < https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-8-199>. Acesso em: 9 jul. 2018.

SIROHI, D. et al. The 3.8 A resolution cryo-EM structure of Zika virus. Science, New York, v. 352, n. 6284, p. 467–470, 2016.

SOUZA, B. S. F. et al. Zika virus infection induces mitosis abnormalities and apoptotic cell death of human neural progenitor cells. Sci Reports [s.l.], v. 6, n. 1, 2016a. Disponível em: <https://www.nature.com/articles/srep39775.pdf.> Acesso em: 9 jul. 2018.

SOUZA, W. V. et al. Microcephaly in Pernambuco State, Brazil: epidemiological characteristics and evaluation of the diagnostic accuracy of cutoff points for reporting suspected cases. Cad. Saude Publica,Rio de Janeiro, v. 32, n. 4, 2016b. Disponível em: <www.scielo.br/pdf/csp/v32n4/en_1678-4464-csp-32-04-e00017216.pdf>.

Acesso em: 29 jun. 2017.

TAN, P-N.; STEINBACH, M.; KUMAR,V. Introduction to Data Mining. 1. ed. Boston: Addison-Wesley, 2006. Disponível em: <www-

users.cs.umn.edu/~kumar001/dmbook/sol.pdf>. Acesso em: 9 jun. 2017.

TIPNEY, H.; HUNTER, L. An introduction to effective use of enrichment analysis software. Hum. genomics, Cairo, v.4, n.3, 2010. Disponível em:

<https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3525973/pdf/1479-7364-4-3- 202.pdf>. Acesso em: 5 set. 2018.

VILLAVECES, J. M. et al. Merging and scoring molecular interactions utilising existing community standards: Tools, use-cases and a case study. Database, Oxford, v. 2015, 2015. Disponível em:

<www.ncbi.nlm.nih.gov/pmc/articles/PMC4316181> Acesso em: 22 fev 2017

VIRUS PATHOGEN RESOURCE. Strain Details for Zika virus Strain ZIKV/H.sapiens/Brazil/PE243/2015-Asian. 2015. Disponível em: <https://tinyurl.com/yaor3k7w>. Acesso em 10 ago. 2018.

VOSSEN, M. T. M. et al. Viral immune evasion: A masterpiece of evolution. Immunogenetics, Berlin, v. 54, n. 8, p. 527–542, 2002.

WELTMAN, J. K. Computer-Assisted Vaccine Design by Analysis of Zika Virus E Proteins Obtained either from Humans or from Aedes Mosquitos.

J. Med. Microbiol. Diagn., Sunnyvale, v. 5, n. 3, p. 3–5, 2016.

ORGANIZAÇÃO MUNDIAL DA SAÚDE. Microcephaly, 2016. Disponível em: <http://www.who.int/mediacentre/factsheets/microcephaly/en/> Acesso em: 25 jun. 2018.

XIA, H. et al. An evolutionary NS1 mutation enhances Zika virus evasion of host interferon induction. Nature Comm., London, 2018. Disponível em:

<www.nature.com/articles/s41467-017-02816-2>. Acesso em: 30 jun. 2018.

XIAO, N. et al. Protr/ProtrWeb: R package and web server for generating various numerical representation schemes of protein sequences. Bioinformatics, Oxford, v. 31, n. 11, p. 1857–1859, 2015.

XIAO, N.; XU, Q-S.; CAO, D-S. protr : R package for generating various

numerical representation schemes of protein sequence. 2015. Disponível em: <https://pdfs.semanticscholar.org/6017/28d4bf539eaa50613c724fc001a966fd4adf.pd f>. Acesso em: 22 jan 2017.

YOON, K. J. et al. Zika-Virus-Encoded NS2A Disrupts Mammalian Cortical Neurogenesis by Degrading Adherens Junction Proteins. Cell Stem Cell, Cambridge, v. 21, n. 3, p. 349–358, 2017.

ZAMMARCHI, L. et al. Zika virus infections imported to Italy: Clinical, immunological and virological findings, and public health implications. J. Clin. Virol., Amsterdam, v. 63, p. 32–35, 2015.

ZARA, A. L. DE S. A. et al. Estratégias de controle do Aedes aegypti: uma revisão. Epidemiol. Serv. Saude, Brasília, v. 25, n. 2, p. 391-404, 2016.

ZHANG, A.; HE, L.; WANG, Y. Prediction of GCRV virus-host protein interactome based on structural motif-domain interactions. BMC Bioinformatics, London, v. 145, n.18, 2017. Disponível em:

<https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-017-1500- 8>. Acesso em: 2 jun. 2018.

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