RT Conference Proceedings
T1 MMRF for proteome annotation applied to human protein disease prediction
A1 García Jiménez, Beatriz
A1 Ledezma Espino, Agapito Ismael
A1 Sanchis de Miguel, María Araceli
AB 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
PB Springer
SN 978-3-642-21294-9 (Print)
SN 978-3-642-21295-6 (Online)
SN 0302-9743 (Print)
SN 1611-3349 (Online)
YR 2011
FD 2011
LK https://hdl.handle.net/10016/11716
UL https://hdl.handle.net/10016/11716
LA eng
NO Proceedings of: 20th International Conference, ILP 2010, Florence, Italy, June 27-30, 2010
DS e-Archivo
RD 17 jul. 2024