Machine Learning algorithms to detect disease risk factors
The work carried out in this WP is about using Cloud distributed Machine Learning algorithms to detect disease risk factors. The goal of the project consists of using both (Big Data) genetic and clinical information of patients to create prediction models, using state-of-the-art algorithms (e.g. deep learning), that can accurately detect high-risk patients.
Team & Collaborators
- Noel Lopes | WP leader
- Elsa Cardoso
- Alberto Junior | PostDoc, C4-UBI (10/2019-02/2021)
- Débora Ferreira | PhD student, C4-UBI (07/2021-08/2022)
Posters in conferences
- Reste-Ferreira D., Oliveira I., Esgalhado J., Sousa A., Amaral A., Martinho A., Verde I., Lourenço O., Fonseca A., Arosa F., Lopes N., Cardoso E.. (2022). Supervised machine learning algorithms that could detect cognitive impairment in elderly. XVII International CICS-UBI Symposium, University of Beira Interior – CICS-UBI, Health Sciences Research Centre. (submitted)
- Esgalhado A., Reste-Ferreira D., Albino S., Sousa A., Amaral A., Martinho A., Oliveira I., Verde I., Lourenço O., Fonseca A., Cardoso E., Arosa F.. (2021). CD45RA, CD8β, and IFN-g represent a novel immune signature of human cognitive function. The XVII Congress of the Iberian Society of Cytometry.
- Alberto Junior. Bacterial evolution and differentiation in infection and disease manifestation in ruminant animals. C4 – RINNOVAR – Research and INNOVation seminAR. November 2019. Covilhã, Portugal.
- Débora Reste-Ferreira attended the course, Hands-on training course: Introduction to Machine Learning using R, 15th-17th November 2021, in the Instituto Gulbenkian de Ciência (IGC), Lisboa, Portugal.