Three-dimensional alpha shapes
VVS '92 Proceedings of the 1992 workshop on Volume visualization
The nature of statistical learning theory
The nature of statistical learning theory
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Prediction of Protein Function Using Common-Neighbors in Protein-Protein Interaction Networks
BIBE '06 Proceedings of the Sixth IEEE Symposium on BionInformatics and BioEngineering
Prediction of DNA-binding residues from sequence
Bioinformatics
CGAL: the Computational Geometry Algorithms Library
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Modeling Protein Interacting Groups by Quasi-Bicliques: Complexity, Algorithm, and Application
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
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Interactions between biomolecules play an essential role in various biological processes. For predicting DNA-binding or protein-binding proteins, many machine-learning-based techniques have used various types of features to represent the interface of the complexes, but they only deal with the properties of a single atom in the interface and do not take into account the information of neighborhood atoms directly. This paper proposes a new feature representation method for biomolecular interfaces based on the theory of graph wavelet. The enhanced graph wavelet features (EGWF) provides an effective way to characterize interface feature through adding physicochemical features and exploiting a graph wavelet formulation. Particularly, graph wavelet condenses the information around the center atom, and thus enhances the discrimination of features of biomolecule binding proteins in the feature space. Experiment results show that EGWF performs effectively for predicting DNA-binding and protein-binding proteins in terms of Matthew's correlation coefficient (MCC) score and the area value under the receiver operating characteristic curve (AUC).