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@INPROCEEDINGS{valko2008learning,
  author = {Michal Valko and Richard Pelikan and Milos Hauskrecht},
  title = {Learning predictive models for multiple heterogeneous proteomic datasources},
  booktitle = {AMIA Summit on Translational Bioinformatics},
  year = {2008},
  month = {March},
  abstract = {Multiple technologies that measure expression levels of protein mixtures
	in the human body offer a potential for detection and understanding
	the disease. The recent increase of these technologies prompts researchers
	to evaluate the individual and combined utility of data generated
	by the technologies. In this work, we study two data sources to measure
	the expression of protein mixtures in the human body: whole-sample
	MS profiling and multiplexed protein arrays.  We investigate the
	individual and combined utility of these technologies by learning
	and testing a variety of classification models on the data from a
	pancreatic cancer study. We show that for the combination of these
	two (heterogeneous) datasets, classification models that work well
	on one of them individually fail on the combination of the two datasets.
	 We study and propose a class of model fusion methods that acknowledge
	the differences and try to reap most of the benefits from their combination.},
  owner = {misovalko},
  timestamp = {2008.01.18}
}