Jaume’s research interests include the development of machine learning methods for large-scale problems and their application to challenging problems, mostly involving biological data. He has published papers on algorithmic advances to improve the scalability of machine learning methods, tackling challenges such as large dimensionality spaces, large sets of records, or the use of data-intensive computing technologies such as GPUs and MapReduce. The main focus of his applied research on biological data is knowledge discovery: analysing the structure of the machine learning models to discover useful knowledge, such as (panels of) biomarkers or functional networks and in this way bring the data mining process closer to the domain experts. Jaume applied his methods to a variety of biological/biomedical domains: the process of germination in plants, cancer in humans or osteoarthritis both in humans and model organisms and multiple data-generating biotechnologies: transcriptomics, proteomics, lipidomics, etc.
Tel: 0191 208 7784
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