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Previous work of the group in this area has dealt with syntactic pattern recognition (mainly grammatical inference), structural pattern recognition (error-tolerant graph matching and probabilistic learning of graph models) and applications in computer vision (object recognition and face recognition). The problem of surface reconstruction from parallel cross sections, typical of 3D medical images, has also been studied, giving rise to a Ph.D. thesis dissertation on this subject. Current interests of SOCO in Pattern Recognition and Computer Vision include dynamic object recognition and tracking from image sequences and bio-inspired hierarchical neural architectures for visual processing.
SOCO works on data and knowledge visualization as a problem that involves artificial pattern recognition as a complement to the natural pattern recognition involved in human visual system. As such, this is a key element of exploratory data mining. Our work focuses on latent variable models, probabilisitic self-organizing systems and Gaussian Processes.
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.