Genomics and metabolomics research for brain tumour diagnosis based on machine learning

  • Authors:
  • Juan M. García-Gómez;Salvador Tortajada;Javier Vicente;Carlos Sáez Xavier Castells;Jan Luts;Margarida Julià-Sapé;Alfons Juan-Císcar;Sabine Van Huffel;Anna Barceló;Joaquín Ariño;Carles Arús;Montserrat Robles

  • Affiliations:
  • ITACA-IBIME, Universidad Politécnica de Valencia, Spain;ITACA-IBIME, Universidad Politécnica de Valencia, Spain;ITACA-IBIME, Universidad Politécnica de Valencia, Spain;Universitat Autònoma de Barcelona, Spain;Katholieke Universiteit Leuven, Dept.of Electrical Engineering, ESAT, SCD, SISTA;Universitat Autònoma de Barcelona, Spain;DSIC, Universidad Politécnica de Valencia, Spain;Katholieke Universiteit Leuven, Dept.of Electrical Engineering, ESAT, SCD, SISTA;Universitat Autònoma de Barcelona, Spain;Universitat Autònoma de Barcelona, Spain;Universitat Autònoma de Barcelona, Spain;ITACA-IBIME, Universidad Politécnica de Valencia, Spain

  • Venue:
  • IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
  • Year:
  • 2007

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Abstract

The incorporation of new biomedical technologies in the diagnosis and prognosis of cancer is changing medicine to an evidence-based diagnosis. We summarize some studies related to brain tumour research in Europe, based on the metabolic information provided by in vivo Magnetic Resonance Spectroscopy (MRS) and transcriptomic profiling observed by DNA microarrays. The first result presents the improvement in brain tumour diagnosis by combining Long TE and Short TE single voxel MR Spectra. Afterwards, a mixture model for binned and truncated data to characterize and classify MRS is reviewed. The classification of Glioblastomas Multiforme and Meningothelial Meningiomas using single-labeling cDNA-based microarrays was studied as proof of principle in the incorporation of genomic information to clinical diagnosis. Finally, we present a Decision Support System for in-vivo classification of brain tumours were the best inferred classifiers are deployed for their clinical use.