AiCaBi
STIC AMSUD
Artificial Intelligence for Cancer Biomarkes Identification
Will the development and integration of meta-heuristics, machine learning and biological annotation data deliver high-quality solutions for the identification of cancer biomarkers?
The current scenario is characterized by a technical capacity to produce large-scale data that goes beyond our analytical capacity for interpretation. The comprehensive characterization of genomic, epigenomic, transcriptomic and proteomic alterations in pathological states, especially when correlated with clinical characteristics, has a great potential to improve the diagnosis and prognosis of diseases, and especially, to enable the practice of personalized medicine in the reality of care activities. This project comprises four big areas: Computational Sciences, Molecular Biology, Bioinformatics and Health Sciences. The aim of this project is to contribute to the personalized medicine seeking, through the development of computational methods and strategies in a Bioinformatic context, to improve the creation of preventive measures for cancer, as well as to advance in diagnosis and prognosis precision of the disease. The main research question that leads this project is: "Will the development and integration of meta-heuristics, machine learning and biological annotation data deliver high-quality solutions for the identification of cancer biomarkers?"
Researchers
Graduate Students/Collaborators
Study Missions
USACH, Chile 
UFRGS, Brazil UFRGS, Brazil 
SU, France Work Missions
USACH, Chile UFRGS, Brazil UFRGS, Brazil USACH, Chile SU, France UFRGS, Brazil UFRGS, Brazil SU, France STIC AMSUD Workshops
2021 - STIC AmSud Workshop
On October 14, 2021, researchers from groups involved with the Stic Amsud project from Chile, Brazil, and France participated in an online and open-to-community workshop.
2022 - STIC AmSud Workshop
From July 18 to 21, 2022, the second Workshop of the AICABI Project was held at the Institute of Informatics/ Center for Biotechnology of the Federal University of Rio Grande do Sul, Porto Alegre, Brazil. The event was realized in a hybrid way with the participation of students and researchers from Chile, France, and Brazil.
STIC AMSUD Meetings / Presentations
2021 - First Meeting
Objective: Planning meeting2021 - Second Meeting
Objective: Presentation of ongoing projects at DIINF/USACH (Chile)2021 - Third Meeting
Objective: Presentation of ongoing work2021 - Fourth Meeting
Objective: Sandwich Ph.D. plan, Gabriel Dominico's study mission, workshop organization, 2021 and 2022 work missions2022 - Fifth Meeting
Objective: Presentation of ongoing work, research meeting at USACH2022 - Sixth Meeting
Objective: Presentation of ongoing work, research meeting at USACHTools & Datasets
Publications
- A multi-objective based clustering for inferring BCR clonal lineages from high-throughput B cell repertoire data ABDOLLAHI, N.; JEUSSET, L.; DE SEPTENVILLE, A. L.; RIPOCHE, H.; DAVI, F.; BERNARDES, J. S. PLOS Computational Biology, v. 18, p. 1-36, 2022.
- An evolutionary algorithm based on parsimony for the multiobjective phylogenetic network inference problem VILLALOBOS-CID, M.; DORN, M.; CONTRERAS, A.; INOSTROZA-PONTA, M. Applied Soft Computing, v. 139, p. 110270, 2023.
- Meta-analyses of host metagenomes from colorectal cancer patients reveal strong relationship between colorectal cancer-associated species ESCALONA, M. A. R.; POLONI, J. F.; KRAUSE, M. J.; DORN, M. Molecular Omics, v. 19, p. 429-444, 2023.
- Interdisciplinary Overview of Lipopeptide and Protein-Containing Biosurfactants JÚNIOR, R. A.; POLONI, J. F.; PINTO, E. S. M.; DORN, M. Genes, v. 14, p. 76, 2023.
- Transcriptomic Analysis of Long Non-Coding RNA during Candida albicans Infection GONÇALVES, G. F.; POLONI, J. F.; DORN, M. Genes, v. 14, p. 251, 2023.
- A Fitness-Based Migration Policy for Biased Random-Key Genetic Algorithms BOIANI, M.; PARPINELLI, R. S.; DORN, M. Applications of Evolutionary Computation, v. 13989, p. 396-410, 2023.
- Multi-Objective Wrapper Differential Evolution with Guided Initial Population for Feature Selection DOMINICO, G.; BERNARDES, J. S.; DORNELES, L. L.; DORN, M. 2023 IEEE Congress on Evolutionary Computation (CEC), p. 1-8, 2023.
- Feature selection reveal peripheral blood parameter's changes between COVID-19 infections patients from Brazil and Ecuador FELTES, B. C.; VIEIRA, I. A.; PARRAGA-ALAVA, J.; MEZA, J.; PORTMANN, E.; TERÁN, L.; DORN, M. Infection, Genetics and Evolution, v. 98, p. 105228, 2022.
- Gene Expression Variation Analysis (GEVA): A new R package to evaluate variations in differential expression in multiple biological conditions NUNES, I. J. G.; FELTES, B. C.; DAVID, M. Z.; DORN, M. Journal of Biomedical Informatics, v. 129, p. 104053, 2022.
- A multi-objective approach for the protein structure prediction problem ALIAGA-ROJAS, S.; VILLALOBOS-CID, M.; DORN, M.; INOSTROZA-PONTA. 2021 40th International Conference of the Chilean Computer Science Society (SCCC), p. 1-8, 2021.
- Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets DORN, M.; GRISCI, B. I.; NARLOCH, P. H.; FELTES, B. C.; AVILA, E.; KAHMANN, A.; ALHO, S. C. PeerJ Computer Science, v. 7, 2021.
- Benchmarking and Testing Machine Learning Approaches with BARRA:CuRDa, a Curated RNA-Seq Database for Cancer Research FELTES, B. C.; POLONI, J. F.; DORN, M. Journal of Computational Biology, v. 28, p. 931-944, 2021.
- Optimisation of Cancer Status Prediction Pipelines using Bio-Inspired Computing BARBACHAN E SILVA, M.; NARLOCH, P. H.; DORN, M.; Ó BROIN, P. 2021 IEEE Congress on Evolutionary Computation (CEC), p. 442-449, 2021.