In proteomics study, Imaging Mass Spectrometry (IMS) is an emerging and very promising
new technique for protein analysis from intact biological tissues. Though it has shown
great potential and is very promising for rapid mapping of protein localization and the
detection of sizeable differences in protein expression, challenges remain in data
processing due to the difficulty of high dimensionality and the fact that the number of
input variables in prediction model is significantly larger than the number of
observations. To obtain a complete overview of IMS data and find trace features based on
both spectral and spatial patterns, one faces a global optimization problem. In this
paper, we propose a weighted elastic net (WEN) model based on IMS data processing needs of
using both the spectral and spatial information for biomarker selection and
classification. Properties including variable selection accuracy of the WEN model are
discussed. Experimental IMS data analysis results show that such a model not only reduces
the number of side features but also helps new biomarkers discovery.