C4.5: programs for machine learning
C4.5: programs for machine learning
Pattern Recognition Letters
Multiple graph matching with Bayesian inference
Pattern Recognition Letters - special issue on pattern recognition in practice V
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Enumerating all connected maximal common subgraphs in two graphs
Theoretical Computer Science
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
MultiProt - A Multiple Protein Structural Alignment Algorithm
WABI '02 Proceedings of the Second International Workshop on Algorithms in Bioinformatics
Multilabel classification via calibrated label ranking
Machine Learning
Protein function prediction based on patterns in biological networks
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Superposition and Alignment of Labeled Point Clouds
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Malware classification based on call graph clustering
Journal in Computer Virology
Geometric graph comparison from an alignment viewpoint
Pattern Recognition
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Graphs are frequently used to describe the geometry and also the physicochemical composition of protein active sites. Here, the concept of graph alignment as a novel method for the structural analysis of protein binding pockets is presented. Using inexact graph-matching techniques, one is able to identify both conserved areas and regions of difference among different binding pockets. Thus, using multiple graph alignments, it is possible to characterize functional protein families and to examine differences among related protein families independent of sequence or fold homology. Optimized algorithms are described for the efficient calculation of multiple graph alignments for the analysis of physicochemical descriptors representing protein binding pockets. Additionally, it is shown how the calculated graph alignments can be analyzed to identify structural features that are characteristic for a given protein family and also features that are discriminative among related families. The methods are applied to a substantial high-quality subset of the PDB database and their ability to successfully characterize and classify 10 highly populated functional protein families is shown. Additionally, two related protein families from the group of serine proteases are examined and important structural differences are detected automatically and efficiently.