Medical technologies: Bioinformatics and biomedical signals
Research Program
Leaders
Where we are
Centre de Recerca en Enginyeria Biomèdica (CREB) UPC
Related websites
Centre de Recerca en Enginyeria Biomèdica (CREB) UPC Researchers from the Biomedical Signals and Systems group offer solutions to companies, hospitals and institutions for the development of technologies applicable to the hospital setting, including their use in surgical instruments.
Scientific objectives
- To develop and validate a set of methods for the functional enrichment of Omics data based on diffusive algorithms.
- To carry out a descriptive statistical analysis of rare diseases in collaboration with the Innovation Department of the Sant Joan de Déu Research Institute (IRSJD) and within the framework of the European Share4Rare project.
- To publish new data analysis methods primarily relating to metabolomics and gene expression. To develop these methods, including enrichment analysis using the previous knowledge of existing annotation databases such as KEGG and Reactome. We will soon publish a new version of MAIT (the first version is available at Bioconductor), including an interactive on-line platform.
- To investigate new clinical risk indices based on non-invasive biosignals.
Area/Field of expertise
Our most outstanding accomplishment within our first line of research is our progress on functional enrichment approaches through diffusion-based techniques applicable to experiments with data from different omic technologies. Our results are published in the open source bioinformatics repository, Bioconductor, on diffusion methods in biology knowledge graph representation in KEGG (Bioconductor - FELLA) offering a methodology for functional enrichment in metabolomic experiments. We have also published standard diffusion algorithms (Bioconductor - diffuStats) as well as various data processing tools, especially in metabolomics (such as Bioconductor - MAIT, CRAN - eRah, CRAN - baitmet) or in genetic association (b2slab - MISS).
Regarding our second research line, we have specialised in multivariate and automatic learning techniques for the analysis of large volumes of clinical, biomedical and biochemical data. Of note is the analysis carried out of a cohort of 1,500 patients diagnosed with heart failure over a 15-year follow-up period; the paper has recently been accepted for publication by JACC: Journal of the American College of Cardiology (IF 16.8, in collaboration with the Germans Trias i Pujol University Hospital, Barcelona). We have also gained expertise, the technical and high-performance computing capacity in the use of Deep Learning algorithms. We could mention, for example, the use of this technology to construct predictive models of protein-compound interaction from recurrent neural network models (Long Short-Term Memory) based on amino acid sequences and SMILES codes, adjusted by TensorFlow (in collaboration with Mind the Byte S.L.).
In terms of signal analysis, we are particularly interested in cardiovascular research using morphology signal analysis in electrocardiography (ECG), RR interval analysis (for heart rate variability [HRV]) and depth of anaesthesia using electroencephalography (EEG) signals, amongst others. Our research goal will be to strengthen the research methods used in the B2SLab regarding the use of linear techniques, symbolic dynamics, information theory, multiscale complexity and multifractality.
Group members
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Jefe de Grupo Senior
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Investigador
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Giovana Elizabeth Gavidia Bovadilla
Investigador
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Investigador
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Técnico
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Investigador
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Investigador
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Investigador pre-doc
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Investigador
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Ayudante de investigación
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Ayudante de investigación
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Investigador pre-doc
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Técnico
Last Publications
- Rojo-López MI, Bermúdez-López M, Castro E, Farràs C, Torres G, Pamplona R, Lecube A, Valdivieso JM, Fernández E, Julve J, Castelblanco E, Alonso N, Antentas M, Barranco-Altirriba M, Perera A, Franch-Nadal J, Granado-Casas M, Mauricio D and On Behalf Of The Ilervas Project Collaborators Mediterranean Diet Is a Predictor of Progression of Subclinical Atherosclerosis in a Mediterranean Population: The ILERVAS Prospective Cohort Study. Nutrients . 16(21): .
- Rojo-López MI, Barranco-Altirriba M, Rossell J, Antentas M, Castelblanco E, Yanes O, Weber RJM, Lloyd GR, Winder C, Dunn WB, Julve J, Granado-Casas M and Mauricio D The Lipidomic Profile Is Associated with the Dietary Pattern in Subjects with and without Diabetes Mellitus from a Mediterranean Area. Nutrients . 16(12): .
- Alegria-Sala, Alba, Lopez, Daniel Marin, Casals, Lluc Canals, Fonollosa J and Macarulla, Marcel The dilemma of variables assumptions in thermal comfort calculations for educational buildings: To Simplify or Not? Journal of Building Engineering . 84: .
Theses
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Multiview pattern recognition methods for data visualization, embedding and clustering
- Author
- Kanaan Izquierdo, Samir
- Institution
- UNIVERSIDAD POLITÉCNICA DE CATALUNYA
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Design and development of methods for paediatric tumours classification using DNA methylation profiles
- Author
- Llano Viles, Joshua
- Institution
- UNIVERSIDAD POLITÉCNICA DE CATALUNYA
News
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Automated learning app improves accessibility to medulloblastoma classification
A team from the Sant Joan de Déu Research Institute, Sant Joan de Déu Hospital, and the Polytechnic University of Catalonia, has developed an artificial intelligence-based application to make the classification of medulloblastoma subgroups more accessible, less costly, and faster
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Retaining talent and fostering public-private partnerships, some of the challenges that is facing the HealthTech sector
Xartec Salut has opened the debate on the main challenges of the Catalan ecosystem to become an international reference in the field of health technologies.