MMRF for Proteome Annotation Applied to Human Protein Disease Prediction
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.
KeywordsRelational Data Mining Human Disease Annotation Multi-Class Relational Decision Tree First-Order Logic Structured Data
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- 4.Clare, A.: Machine learning and data mining for yeast functional genomics. PhD thesis, University of Wales, Aberystwyth (2003)Google Scholar
- 6.García, B., et al.: Modular Multi-Relational Framework for Gene Group Function Prediction.. In: Online Proceedings ILP (2009)Google Scholar
- 17.Smedley, D., et al.: BioMart-biological queries made easy. BMC Genomics 10 (2009)Google Scholar
- 20.Tran, T.N., Satou, K., Ho, T.-B.: Using inductive logic programming for predicting protein-protein interactions from multiple genomic data. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 321–330. Springer, Heidelberg (2005), http://dx.doi.org/10.1007/11564126_33 CrossRefGoogle Scholar