@inproceedings{Garcia_et.al._ilp10,
  author    = {Beatriz Garc\'{\i}a-Jim{\'e}nez and
               Agapito Ledezma and
               Araceli Sanchis},
  title     = {MMRF for Proteome Annotation Applied to Human Protein Disease
               Prediction},
  booktitle = {Inductive Logic Programming - 20th International Conference,
               ILP 2010, Florence, Italy, June 27-30, 2010. Revised Papers},
  year      = {2010},
  pages     = {67-75},
  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.},
  series    = {Lecture Notes in Artificial Intelligence},
  ee        = {http://dx.doi.org/10.1007/978-3-642-21295-6_11}
}

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