Bioinformatics applied to personalized medicine: from diagnosis to treatment of diseases


Jan/2017 to Dez/2018

In this research project, we are interested in the development and application of strategies to combine, explore, and analyze genomic data to identify new biomarkers of diagnosis and/or prognosis value, or that could be used as potential therapeutic targets. Using Bioinformatics approaches, and the union of combined efforts from researchers with expertise in Computer, Biological, and Health Sciences, we intend to address research issues related to cardiovascular diseases and cancer, focusing on the translational potential of in silico findings. Hence, this project aims to generate scientific knowledge with potential clinical use, which could bring important benefits to the care and preventive activities in the Brazilian Unified Health System (SUS), by improving the process of diagnosis, prognosis elaboration, and therapeutic planning of patients with these pathologies.
Within this theme, this project will specifically address the following research questions:
i) Detection of molecular differences between physiological and pathological cardiac hypertrophy with potential therapeutic application in the treatment of heart failure.
ii) Identification of biomarkers of unstable atherosclerotic disease.
iii) Characterization of molecular alterations in breast, ovary, and colon tumors in patients with germinal mutations in genes with a predisposition to cancer.
iv) Evaluation of the pathogenicity of gene variants of uncertain significance in cancer.

Researches:

Publications:


Journals:

  • Grisci, B.I.; Feltes, B.C.; Dorn, M. Neuroevolution as a Tool for Microarray Gene Expression Pattern Identification in Cancer Research. Journal of Biomedical Informatics, v. 89, p. 122-133, 2019. DOI: http://dx.doi.org/10.1016/j.jbi.2018.11.013
  • Feltes, B.C.; Chandelier, E.B.; Grisci, B.I.; Dorn, M. CuMiDa: An Extensively Curated Microarray Database for Benchmarking and Testing of Machine Learning Approaches in Cancer Research. Journal of Computational Biology, 2019. DOI: https://doi.org/10.1089/cmb.2018.0238
  • Arantes, P. R.; Polêto, M. Depólo; John, E.B. O.; Pedebos, C.; Grisci, B.I. ; Dorn, M.; Verli, H. Development of GROMOS-Compatible Parameter Set for Simulations of Chalcones and Flavonoids. Journal of Physical Chemistry B, v. 123, p. 1-20, 2019.
    DOI: http://dx.doi.org/10.1021/acs.jpcb.8b10139
  • Leonhart, P.F.; Oliveira, E.S.; Ligabue-Braun, R.L.; Dorn, M. A biased random key genetic algorithm for the protein-ligand docking problem. Soft Computing, v. 22, p. 1, 2018.
    DOI: http://dx.doi.org/10.1007/s00500-018-3065-5
  • Feltes, B.C. Architects meets Repairers: The interplay between homeobox genes and DNA repair. DNA Repair, v. 73, p. 34-48, 2018. DOI: http://dx.doi.org/10.1016/j.dnarep.2018.10.007
  • Ligabue-Braun, R.; Borguesan, B.; Verli, H.; Krause, M.J.; Dorn, M. Everyone Is a Protagonist: Residue Conformational Preferences in High-Resolution Protein Structures. Journal of Computational Biology, v. 25, p. 451-465, 2018.
    DOI: http://dx.doi.org/10.1089/cmb.2017.0182
  • Parraga-Alava, J.; Dorn, M.; Inostroza-Ponta, M. A multi-objective gene clustering algorithm guided by apriori biological knowledge with intensification and diversification strategies. BioData Mining, v. 11, p. 1, 2018.
    DOI: http://dx.doi.org/10.1186/s13040-018-0178-4
  • Polêto, M.D.; Rusu, V.H.; Grisci, B.I.; Dorn, M.; Lins, R.D.; Verli, H. Aromatic Rings Commonly Used in Medicinal Chemistry: Force Fields Comparison and Interactions With Water Toward the Design of New Chemical Entities. Frontiers in Pharmacology, v. 9, p. 395, 2018.
    DOI: http://dx.doi.org/10.3389/fphar.2018.00395
  • Feltes, B.C.; Grisci, B.I.; Poloni, J.F.; Dorn, M. Perspectives and Applications of Machine Learning for Evolutionary Developmental Biology. Molecular BioSystems, v. 14, p. 1, 2018.
    DOI: http://dx.doi.org/10.1039/C8MO00111A


Proceedings:

  • Leonhart, P.F.; Dorn, M. A Biased Random Key Genetic Algorithm with Local Search Chains for Molecular Docking. 22nd International Conference on the Applications of Evolutionary Computation, 2019, Leipzig. DOI: #
  • Narloch, P.H.; Dorn, M. A Knowledge Based Differential Evolution Algorithm for Protein Structure Prediction. 22nd International Conference on the Applications of Evolutionary Computation, 2019, Leipzig. DOI: #
  • Borguesan, B.; Narloch, P.H.; Inostroza-Ponta, M.; Dorn, M. A Genetic Algorithm Based on Restricted Tournament Selection for the 3D-PSP Problem. 2018 IEEE Congress on Evolutionary Computation (CEC), 2018, Rio de Janeiro. DOI: https://doi.org/10.1109/CEC.2018.8477721
  • de Lima Correa, L.; Dorn, M. A Knowledge-Based Artificial Bee Colony Algorithm for the 3-D Protein Structure Prediction Problem. 2018 IEEE Congress on Evolutionary Computation (CEC), 2018, Rio de Janeiro. DOI: https://doi.org/10.1109/CEC.2018.8477863
  • Grisci, B. I.; Feltes, B. C.; Dorn, M. Microarray Classification and Gene Selection with FS-NEAT. 2018 IEEE Congress on Evolutionary Computation (CEC), 2018, Rio de Janeiro. DOI: https://doi.org/10.1109/CEC.2018.8477813
  • Villalobos-Cid, M.; Dorn, M.; Inostroza-Ponta, M. Understanding the Relationship Between Decision and Objective Space in the Multi-Objective Phylogenetic Inference Problem. In: 2018 IEEE Congress on Evolutionary Computation (CEC), 2018, Rio de Janeiro. DOI: https://doi.org/10.1109/CEC.2018.8477689
  • Parraga-Alava, J.; Dorn, M.; Inostroza-Ponta, M. Using local search strategies to improve the performance of NSGA-II for the Multi-Criteria Minimum Spanning Tree problem. In: 2017 IEEE Congress on Evolutionary Computation (CEC), 2017, Donostia. DOI: https://doi.org/10.1109/CEC.2017.7969432
  • de Lima Correa, L.; Inostroza-Ponta, M.; Dorn, M. An evolutionary multi-agent algorithm to explore the high degree of selectivity in three-dimensional protein structures. In: 2017 IEEE Congress on Evolutionary Computation (CEC), 2017, Donostia. DOI: https://doi.org/10.1109/cec.2017.7969431
  • Oliveira, M.; Borguesan, B.; Dorn, M. A Self-Adapting Differential Evolution algorithm with a loop Structure Pattern Library for the PSP problem. In: 2017 IEEE Congress on Evolutionary Computation (CEC), 2017, Donostia. DOI: https://doi.org/10.1109/cec.2017.7969429
  • de Faria Alixandre, B.F.; Dorn, M. D-BRKGA: A Distributed Biased Random-Key Genetic Algorithm. In: 2017 IEEE Congress on Evolutionary Computation (CEC), 2017, Donostia. DOI: https://doi.org/10.1109/cec.2017.7969467


Datasets:


Softwares and Libraries:

Finantial Support: