One line of SOCO's research concerns probabilistic unsupervised models for high dimensional data clustering and visualization. Specifically, we are interested in latent, non-linear manifold learning models for data visualization and clustering, and matrix factorization methods for blind source separation, with application to real-world problems in the areas of clinical medicine, ecology, and business processes, including collaborations with the Neural Computation Group at Liverpool John Moores University (UK) [go] and the GABRMN [go] and Systems Pharmacology and Bioinformatics [go] research groups at Universitat Autònoma de Barcelona (UAB).
The first five describe methodologies with a similar goal: the resolution of complex problems that cannot be efficiently solved by means of traditional computational methods (hard computing). Feature Selection and Extraction deals with problems of data dimensionality reduction that are present in all the previous lines. Finally, Pattern Recognition and Computer Vision copes with a huge application field where the performance of AI systems is usually worse than the human one, and where the soft computing techniques have great potential. Related to it, Data and Knowledge Visualization concerns ways in which Soft Computing complements human vision in problems of exploratory Data Mining.
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SOCO's current main line of research is in the areas of biomedicine and bioinformatics. Our work involves the development of robust Computational Intelligence techniques and their application to data-based medical diagnosis and prognosis, mainly in the area of neuro-oncology, and to protein sequence analysis for pharmaco-proteomics. In the former problem, SOCO collaborates with the Neural Computation Research Group [go] at Liverpool John Moores University [go] in U.K., and with the Grup d'Aplicacions Biomèdiques de la Ressonància Magnètica Nuclear (GABRMN) [go] at Universitat Autònoma de Barcelona (UAB) [go]. In the latter problem, SOCO is currently leading a MINECO R+D project (TIN2012-31377), titled "KAPPA AIM: Knowledge Acquisition in Pharmacoproteomics using Advanced Artificial Intelligence Methods", in collaboration with the Systems Pharmacology and Bioinformatics group [go] at UAB. SOCO also collaborates in the SHOCKOMICS European project [go] providing data science in a multiscale approach to the identification of molecular biomakers in acute heart failure induced by shock. We also investigate major depressive disorders. The prevalence of depression, its social and personal costs and its recurrent characteristics, put heavy constraints on the ability of public healthcare systems to provide sufficient support for patients with depression. Current research at SOCO includes the design and development of a remote intelligent monitoring and prediction system that performs an automatic assessment on the progress of the patient in a short-term basis, carrying out prediction of possible reoccurrence or relapses.
SOCO also collaborated with the Servei de Medicina Interna de l'Hospital General de la Vall d'Hebron [go] in a study of the relevance of several clinical and immunological variables for the prediction of cancer in patients with idiopathic inflammatory myopathies.