Career Profile

Patrick Terrematte received a B.S. in systems analysis from Federal Institute of Education, Science and Technology of Rio Grande do Norte (IFRN), Brazil, in 2011, and a M.S. degree in systems and computation from Federal University of Rio Grande do Norte (UFRN), Brazil, in 2013. He has a Ph.D. in bioinformatics from UFRN, Brazil, in 2022. Currently, he is assistant professor at UFRN, Metropolis Digital Institute (IMD) in Brazil. From 2016 to 2022, he was an assistant professor of Computing Engineering at Federal Rural University of Semi-arid (UFERSA), Department of Engineering and Technology in Brazil. His research interests include artificial intelligence, explainable machine learning, feature selection, bioinformatics, survival analysis, systems biology, and fuzzy logic. Currently, he is member of IEEE Computational Intelligence Society (CIS).

Experiences

Lecturer (Assistant Professor)

2022 - Present
Digital Metropolis Institute, Federal University of Rio Grande do Norte (UFRN), Brazil.

Assistant Professor

2016 - 2022
UFERSA / Department of Engineering and Technology / Brazil

Research interests:

  • Introduction to informatics
  • Numerical Analysis
  • Theory of Computation

Certifications

Huawei Certification HCIA - Artificial Intelligence - AI

2021 - 2024
Huawei Certification

Huawei Certified ICT Associate-AI.

Training and certificating engineers who can use algorithms, such as machine learning and deep learning algorithms, to design and develop AI products and solutions and make improvement through innovation.

Publications

Machine Learning. Feature Selection. Gene Signatures. Tools for Bioinformatics. Databases.

  • An Integrated Data Analysis Using Bioinformatics and Random Forest to Predict Prognosis of Patients with Squamous Cell Carcinoma
  • Débora V. C. Lima; Patrick Terrematte; Beatriz Stransky; Adrião D. Dória Neto.
    IEEE Acces, (2024). doi:10.1109/access.2024.3392277.
  • Machine Learning Gene Signature to Metastatic ccRCC Based on ceRNA Network
  • Epitácio Farias; Patrick Terrematte; Beatriz Stransky.
    Int. J. Mol. Sci. 2024, 25(8), 4214, (2024). doi:10.3390/ijms25084214.
  • GENTLE - a novel bioinformatics tool for generating features and building classifiers from T cell repertoire cancer data
  • Dhiego Souto Andrade; Patrick Terrematte; César Rennó-Costa; Alona Zilberberg; Sol Efroni.
    BMC Bioinformatics 24, 32 (2023) doi:10.1186/s12859-023-05155-w
  • dbPepVar - a novel cancer proteogenomics database
  • Lucas Marques Da Cunha; Patrick Terrematte; Tayna Da Silva Fiuza; Vandeclécio Lira Da Silva; Sandro José De Souza; Gustavo Antônio De Souza.
    IEEE Access, v. 1, p. 1-1, (2022) doi:10.1109/ACCESS.2022.3201897
  • A Novel Machine Learning 13-Gene Signature - Improving Risk Analysis and Survival Prediction for Clear Cell Renal Cell Carcinoma Patients
  • Patrick Terrematte; Dhiego Souto Andrade; Josivan Justino; Beatriz Stransky; Daniel Sabino A. de Araújo; Adrião D. Dória Neto.
    Cancers 2022, 14, 2111, (2022) doi:doi:10.3390/cancers14092111

    Skills & Proficiency

    Python, Streamlit

    R, Shiny

    Java, Spring, Hibernate

    MLOps