Pharmacology Machine Learning Group
Our group is interested in developing algorithms, techniques and mathematical models that can be applied to the medical field and pharmaceutical sciences. Currently, one of the main axes of our research is biomarker discovery, where in collaboration with different research groups from around the world, we developed techniques to tackle reproducibility issues. For this objective, we have developed techniques that involve machine learning like recursive ensemble feature selection, and other based on deep learning with success in omics analysis.
Introduction to Machine Learning for Pharmacology (Videos):
Github:
Key Group Publications:
Rojas-Velazquez, D., Kidwai, S., Liu, T. C., El-Yacoubi, M. A., Garssen, J., Tonda, A., & Lopez-Rincon, A. (2025). Understanding Parkinson's: The microbiome and machine learning approach. Maturitas, 193, Article 108185.
Rojas-Velazquez, D., Kidwai, S., Kraneveld, A. D., Tonda, A., Oberski, D., Garssen, J., & Lopez-Rincon, A. (2024). Methodology for biomarker discovery with reproducibility in microbiome data using machine learning. BMC Bioinformatics, 25(1).
Peralta-Marzal, L. N., Rojas-Velazquez, D., Rigters, D., Prince, N., Garssen, J., Kraneveld, A. D., Perez-Pardo, P., & Lopez-Rincon, A. (2024). A robust microbiome signature for autism spectrum disorder across different studies using machine learning. Scientific Reports, 14(1), 814.
Kidwai, S., Barbiero, P., Meijerman, I., Tonda, A., Perez-Pardo, P., Lio, P., van der Maitland-Zee, A. H., Oberski, D. L., Kraneveld, A. D., & Lopez-Rincon, A. (2023). A robust mRNA signature obtained via recursive ensemble feature selection predicts the responsiveness of omalizumab in moderate-to-severe asthma. Clinical and Translational Allergy, 13(11), e12306. Article e12306.
Lopez-Rincon, A., Tonda, A., Elati, M., Schwander, O., Piwowarski, B., & Gallinari, P. (2018). Evolutionary optimization of convolutional neural networks for cancer miRNA biomarkers classification. Applied Soft Computing Journal, 65, 91-100.
Lopez-Rincon, A., Martinez-Archundia, M., Martinez-Ruiz, G. U., Schoenhuth, A., & Tonda, A. (2019). Automatic discovery of 100-miRNA signature for cancer classification using ensemble feature selection. BMC Bioinformatics, 20(1), Article 480.
Lopez-Rincon, A., Mendoza-Maldonado, L., Martinez-Archundia, M., Sch枚nhuth, A., Kraneveld, A. D., Garssen, J., & Tonda, A. (2020). Machine learning-based ensemble recursive feature selection of circulating mirnas for cancer tumor classification. Cancers, 12(7), 1-27. Article 1785.
Metselaar, P. I., Mendoza-Maldonado, L., Li Yim, A. Y. F., Abarkan, I., Henneman, P., te Velde, A. A., Sch枚nhuth, A., Bosch, J. A., Kraneveld, A. D., & Lopez-Rincon, A. (2021). Recursive ensemble feature selection provides a robust mRNA expression signature for myalgic encephalomyelitis/chronic fatigue syndrome. Scientific Reports, 11(1), 1-11. Article 4541.