ACDBio: The Biological Data Computational Analysis group at ICMC/USP, IFSP, and Barretos Cancer Hospital

Authors

  • Adenilso Simao University of São Paulo
  • Adriane Feijó Evangelista Barretos Cancer Hospital and Oswaldo Cruz Foundation
  • Alfredo Guilherme Souza University of São Paulo
  • Cynthia de Oliveira Lage Ferreira University of São Paulo
  • Jorge Francisco Cutigi Federal Institute of São Paulo
  • Paulo Henrique Ribeiro Federal Institute of São Paulo
  • Rodrigo Henrique Ramos Federal Institute of São Paulo

DOI:

https://doi.org/10.5753/jidm.2024.2622

Keywords:

Bioinformatics, Biological Data Analysis, Computer Science

Abstract

Recent advances in biological and health technology have resulted in vast digital data. However, the major challenge is interpreting such data to find valuable knowledge. For this, using computing is essential and mandatory since quick data processing and analysis, allied with knowledge extraction techniques, enable working effectively with large biological datasets. In this context, the ACDBio group works with the computational analysis of biological data from different sources, aiming to find new information and knowledge in data or answer questions that are not yet known. So far, the group has worked on several challenging topics, such as identifying significant genes for cancer topological analysis of genes in interaction networks, among others. The group uses computational techniques such as complex networks and their algorithms, machine learning, and topological data analysis. This article aims to present the ACDBio group, and the main research topics worked on by its members. We also present the main results and future work expected by the group.

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Published

2024-02-17

How to Cite

Simao, A., Feijó Evangelista, A., Guilherme Souza, A., de Oliveira Lage Ferreira, C., Francisco Cutigi, J., Henrique Ribeiro, P., & Henrique Ramos, R. (2024). ACDBio: The Biological Data Computational Analysis group at ICMC/USP, IFSP, and Barretos Cancer Hospital. Journal of Information and Data Management, 15(1), 61–68. https://doi.org/10.5753/jidm.2024.2622

Issue

Section

Brazilian Bioinformatics Research Groups