Bioinformatics of infectious and chronic diseases at the Center for Technological Development in Health of Fiocruz


  • Nicolas Carels Fundação Oswaldo Cruz
  • Gilberto Ferreira da Silva Fundação Oswaldo Cruz
  • Carlyle Ribeiro Lima Fundação Oswaldo Cruz
  • Franklin Souza da Silva Fundação Oswaldo Cruz
  • Milena Magalhães Fundação Oswaldo Cruz
  • Ana Emília Goulart Lemos Fundação Oswaldo Cruz | INCA | PICTIS



Information retrieval, Data Mining and Integration, System Modeling


One of the bioinformatics purposes is data mining and integration to solve fundamental scientific challenges. We have been investigating biological systems including viruses, bacteria, fungi, protozoans, plants, insects, and animals with such concern. Gradually, we moved from basic questions on genome organization to application in infectious and chronic diseases by integrating interactome and RNA-seq data to modeling techniques such as Flux Balance Analysis, structural modeling, Boolean modeling, system dynamics, and computation biology in a system biology perspective. At the moment, we focus on the rational therapy of cancer assisted by RNA sequencing, network modeling, and structural modeling.


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Author Biographies

Nicolas Carels, Fundação Oswaldo Cruz



Gilberto Ferreira da Silva, Fundação Oswaldo Cruz

Data Base


Carlyle Ribeiro Lima, Fundação Oswaldo Cruz



Franklin Souza da Silva, Fundação Oswaldo Cruz




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Antunes, L. C. M., Han, J., Pan, J., Moreira, C. J. C., Azambuja, P., Borchers, C. H., and Carels, N. (2013). Metabolic signatures of triatomine Vectors of Trypanosoma cruzi unveiled by metabolomics. Plos One, 8:e77283–12.

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Barbosa-Silva, A., Magalhães, M., da Silva, G. F., da Silva, F. A. B., Carneiro, F. R. G., and Carels, N. (2022). A data science approach for the identification of molecular signatures of aggressive cancers. Cancers, 14(9):2325.

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Carels, N. (2005b). The maize gene space is compositionally compartimentalized. FEBS Letters, 579(18):3867–3871.

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Carels, N. and Frias, D. (2013a). A statistical method without training step for the classification of coding Frame in transcriptome sequences. Bioinf Biol Insights, 7:35–54.

Carels, N. and Frias, D. (2013b). BIOMAT 2012, chapter The contribution of stop codon frequency and purine bias to the classification of coding sequences, pages 301–322. World Scientific.

Carels, N., Gumiel, M., da Mota, F. F., Moreira, C. J., and Azambuja, P. (2017). A metagenomic analysis of bacterial microbiota in the digestive tract of triatomines. Bioinf Biol Insights, 11:117793221773342.

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Carels, N. and Ponce de Leon, M. (2015). An interpretation of the ancestral codon from Miller’s amino acids and nucleotide correlations in modern coding sequences. Bioinf Biol Insights, 9:37–47.

Carels, N., Tilli, T., and Tuszynski, J. A. (2015). A computational strategy to select optimized protein targets for drug development toward the control of cancer diseases. Plos One, 10:e0115054–16.

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How to Cite

Carels, N., Ferreira da Silva, G., Ribeiro Lima, C., Souza da Silva, F., Magalhães, M., & Emília Goulart Lemos, A. (2024). Bioinformatics of infectious and chronic diseases at the Center for Technological Development in Health of Fiocruz. Journal of Information and Data Management, 15(1), 51–60.



Brazilian Bioinformatics Research Groups