@INPROCEEDINGS{Garcia2010,
  author = {Beatriz Garcia and Agapito Ledezma and Araceli Sanchis.},
  title = {MMRF for Proteome Annotation Applied to Human Protein Disease Prediction.},
  booktitle = {Proceedings of the 20th International Conference on Inductive Logic
	Programming (ILP 2010).},
  year = {2010},
  series = {Lecture Notes in Artificial Intelligence},
  pages = {In Press},
  month = {June},
  abstract = {Biological processes where every gene and protein participates is
	an essential knowledge for designing disease treatments. Nowadays,
	these annotations are still unknown for many genes and proteins.
	Since making annotations from in-vivo experiments is costly, computational
	predictors are needed for different kinds of annotation such as metabolic
	pathway, interaction network, protein family, tissue, disease and
	so on. Biological data has an intrinsic relational structure, including
	genes and proteins, which can be grouped by many criteria. This hinders
	the possibility of finding good hypotheses when attribute-value representation
	is used. Hence, we propose the generic Modular Multi-Relational Framework
	(MMRF) to predict different kinds of gene and protein annotation
	using Relational Data Mining (RDM). The specific MMRF application
	to annotate human protein with diseases verifies that group knowledge
	(mainly protein-protein interaction pairs) improves the prediction,
	particularly doubling the area under the precision-recall curve.},
  location = {Firenze, Italy}
}

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