Semi-supervised learning of sparse linear models in mass spectral imaging

Authors:
Fabian Ojeda;Marco Signoretto;Raf Van De Plas;Etienne Waelkens;Bart De Moor;Johan A. K. Suykens
Affiliations:
ESAT, SCD, SISTA, Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium;ESAT, SCD, SISTA, Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium;ESAT, SCD, SISTA, Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium and ProMeta, Interfaculty Centre for Proteomics and Metabolomics, Katholieke Universiteit Le ...;Laboratory for Phosphoproteomics, Katholieke Universiteit Leuven, Leuven, Belgium and ProMeta, Interfaculty Centre for Proteomics and Metabolomics, Katholieke Universiteit Leuven, Leuven, Belgium;ESAT, SCD, SISTA, Department of Electrical Engineering, Katholieke Universiteit Leuven,Leuven, Belgium and ProMeta, Interfaculty Centre for Proteomics and Metabolomics, Katholieke Universiteit Leu ...;ESAT, SCD, SISTA, Department of Electrical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium
Venue:
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Year:
2010

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Abstract

We present an approach to learn predictive models and perform variable selection by incorporating structural information from Mass Spectral Imaging (MSI) data. We explore the use of a smooth quadratic penalty to model the natural ordering of the physical variables, that is the mass-to-charge (m/z) ratios. Thereby, estimated model parameters for nearby variables are enforced to smoothly vary. Similarly, to overcome the lack of labeled data we model the spatial proximity among spectra by means of a connectivity graph over the set of predicted labels. We explore the usefulness of this approach in a mouse brain MSI data set.