Abstract
Protein-protein interactions play an important role in many fundamental biological processes. Computational approaches for predicting protein-protein interactions are essential to infer the functions of unknown proteins, and to validate the results obtained of experimental methods on protein-protein interactions. We have developed an approach using Inductive Logic Programming (ILP) for protein-protein interaction prediction by exploiting multiple genomic data including protein-protein interaction data, SWISS-PROT database, cell cycle expression data, Gene Ontology, and InterPro database. The proposed approach demonstrates a promising result in terms of obtaining high sensitivity/specificity and comprehensible rules that are useful for predicting novel protein-protein interactions. We have also applied our method to a number of protein-protein interaction data, demonstrating an improvement on the expression profile reliability (EPR) index.
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Keywords
- Inductive Logic Program
- Domain Pair
- Protein Secondary Structure Prediction
- Inductive Logic Program System
- Bottom Clause
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Tran, T.N., Satou, K., Ho, T.B. (2005). Using Inductive Logic Programming for Predicting Protein-Protein Interactions from Multiple Genomic Data. In: Jorge, A.M., Torgo, L., Brazdil, P., Camacho, R., Gama, J. (eds) Knowledge Discovery in Databases: PKDD 2005. PKDD 2005. Lecture Notes in Computer Science(), vol 3721. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11564126_33
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DOI: https://doi.org/10.1007/11564126_33
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