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
• Alberto Junior | Grantee, C4-UBI

Publications

To appear.

Related publications

• Lopes, N., & Ribeiro, B. (2017). Novel Trends in Scaling Up Machine Learning Algorithms. In 16th IEEE International Conference on Machine Learning and Applications (pp. 632-636). IEEE.
• Lopes, N., & Ribeiro, B. (2015). Machine learning for adaptive many-core machines: a practical approach.
• Lopes, N., Correia, D., Pereira, C., Ribeiro, B., & Dourado, A. (2012). An incremental hypersphere learning framework for protein membership prediction. In International Conference on Hybrid Artificial Intelligence Systems (pp. 429-439). Springer, Berlin, Heidelberg.

Related activities

• Alberto Junior, Bacterial evolution and differentiation in infection and disease manifestation in ruminant animals. C4 – RINNOVAR – Research and INNOVation seminAR, November 7, 2019, Covilhã, Portugal