Research Interests

I have strong interest in topics related to:

  • Bioinformatics
  • Computational Biology
  • Data Science
  • Machine Learning
  • Systems Biology

Research Groups

I've participated as collaborator in the research groups:

Current

Past:

Research

Research interests and projects

In broad terms, I am highly interested in developing novel computational methods and strategies for making sense of genomic and clinical data. My research focuses mainly on bioinformatics and computational biology, with emphasis on machine learning and its applications to Biology and Medicine. In both areas, we are facing a phenomenon of data deluge, and computational methods have been essential to explore, understand, and promote new scientific discoveries from this huge volume of data.

I have worked with a variety of problems within these research fields. Recent projects are related (but not limited) to the following topics:

  • Computational inference and analysis of gene regulatory networks
  • Integration of multi-omics data to study complex human diseases
  • Machine learning models in clinical and biological data science
  • Metanalysis and feature selection techniques to identify disease-related genes
  • Network-based approaches to investigate diseases mechanisms and biomarkers
  • Prediction and analysis of microRNAs' target genes

On the computational side, my interests are mainly on:

  • Supervised, unsupervised and semi-supervised learning
  • Machine learning on graphs
  • Feature selection methods
  • Machine learning with multi-view data
  • Ensemble learning and the 'wisdom of crowds' theory

I'm deeply interested both in the computational challenges and in the biological/clinical relevance and practical application of computationally derived insights. Thus, I enjoy participating in interdisciplinary projects, collaborating with researchers from Biology, Medicine, and related areas in solving challenging real-word problems.

If you would like to propose a collaboration, do not hesitate to contact me.

Projects

Below I highlight some ongoing and recent projects. For a complete relation of past and current projects, please see my CV at Lattes Platform.

  • 2022 - present. Aprendizado de máquina com dados heterogêneos para modelos preditivos mais precisos em saúde e genômica. Coordination: Profa. Mariana Recamonde Mendoza. Funded by CNPq.
  • 2022 - present. MARCS: Modelos de Aprendizado de máquina Robustos e Confiáveis para a Saúde. Coordination: Profa. Mariana Recamonde Mendoza. Funded by FAPERGS.
  • 2020 - present. Diagnóstico e predição clínica em oncologia: análise integrativa de dados ômicos através de modelos complexos de aprendizado de máquina. Coordination: Profa. Mariana Recamonde Mendoza. In collaboration with HCPA. Funded by CNPq/AWS.
  • 2020 - 2022. CIDIA-19 - CIência de Dados e Inteligência Artificial para combater a COVID-19. Coordinator: Prof. João Comba. In collaboration with HCPA and HMV. Funded by FAPERGS.