Shortest-Path Kernels on Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Nonstationary kernel combination
ICML '06 Proceedings of the 23rd international conference on Machine learning
Exploiting inter-gene information for microarray data integration
Proceedings of the 2007 ACM symposium on Applied computing
Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Graph kernels between point clouds
Proceedings of the 25th international conference on Machine learning
Proceedings of the 25th international conference on Machine learning
Reducing the Dimensionality of Vector Space Embeddings of Graphs
MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Propositionalisation of Profile Hidden Markov Models for Biological Sequence Analysis
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Reducing the dimensionality of dissimilarity space embedding graph kernels
Engineering Applications of Artificial Intelligence
A cube framework for incorporating inter-gene information into biological data mining
International Journal of Data Mining and Bioinformatics
Graph kernels based on tree patterns for molecules
Machine Learning
Graph matching using the interference of discrete-time quantum walks
Image and Vision Computing
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Recommendation as link prediction: a graph kernel-based machine learning approach
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
An automatic translation of tags for multimedia contents using folksonomy networks
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Within-Network Classification Using Local Structure Similarity
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
A kernel approach to comparing distributions
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Journal of Management Information Systems
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
An application of kernel methods to gene cluster temporal meta-analysis
Computers and Operations Research
Discovering relations among GO-annotated clusters by Graph Kernel methods
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
Graph embedding using quantum commute times
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
Evaluating graph kernel methods for relation discovery in GO-annotated clusters
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
A family of novel graph kernels for structural pattern recognition
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Gene function prediction with gene interaction networks: a context graph kernel approach
IEEE Transactions on Information Technology in Biomedicine
Measuring protein structural similarity by maximum common edge subgraphs
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
Characterizing structural relationships in scenes using graph kernels
ACM SIGGRAPH 2011 papers
Graph embedding using a quasi-quantum analogue of the hitting times of continuous time quantum walks
Quantum Information & Computation
Inexact graph matching based on kernels for object retrieval in image databases
Image and Vision Computing
Weisfeiler-Lehman Graph Kernels
The Journal of Machine Learning Research
A random walk kernel derived from graph edit distance
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
Adaptive matching based kernels for labelled graphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Towards the unification of structural and statistical pattern recognition
Pattern Recognition Letters
A new protein graph model for function prediction
Computational Biology and Chemistry
The Journal of Machine Learning Research
Predicting Protein Function by Multi-Label Correlated Semi-Supervised Learning
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Graph embedding in vector spaces by node attribute statistics
Pattern Recognition
PRIB'12 Proceedings of the 7th IAPR international conference on Pattern Recognition in Bioinformatics
Fuzzy Sets and Systems
Entity disambiguation in anonymized graphs using graph kernels
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Structural detection of android malware using embedded call graphs
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
Subtree selection in kernels for graph classification
International Journal of Data Mining and Bioinformatics
Depth-based complexity traces of graphs
Pattern Recognition
International Journal of Knowledge Discovery in Bioinformatics
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Motivation: Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid motifs, interaction partners or phylogenetic profiles. We present a new approach that combines sequential, structural and chemical information into one graph model of proteins. We predict functional class membership of enzymes and non-enzymes using graph kernels and support vector machine classification on these protein graphs. Results: Our graph model, derivable from protein sequence and structure only, is competitive with vector models that require additional protein information, such as the size of surface pockets. If we include this extra information into our graph model, our classifier yields significantly higher accuracy levels than the vector models. Hyperkernels allow us to select and to optimally combine the most relevant node attributes in our protein graphs. We have laid the foundation for a protein function prediction system that integrates protein information from various sources efficiently and effectively. Availability: More information available via www.dbs.ifi.lmu.de/Mitarbeiter/borgwardt.html. Contact: borgwardt@dbs.ifi.lmu.de