Graph kernels between point clouds
Proceedings of the 25th international conference on Machine learning
A Class of Evolution-Based Kernels for Protein Homology Analysis: A Generalization of the PAM Model
ISBRA '09 Proceedings of the 5th International Symposium on Bioinformatics Research and Applications
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Regularized Kernel Local Linear Embedding on Dimensionality Reduction for Non-vectorial Data
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
Twin kernel embedding with relaxed constraints on dimensionality reduction for structured data
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Predicting miRNA-mediated gene silencing mode based on miRNA-target duplex features
Computers in Biology and Medicine
RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
A family of feed-forward models for protein sequence classification
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
PRIB'12 Proceedings of the 7th IAPR international conference on Pattern Recognition in Bioinformatics
Efficient methods for robust classification under uncertainty in kernel matrices
The Journal of Machine Learning Research
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Motivation: This work aims to develop computational methods to annotate protein structures in an automated fashion. We employ a support vector machine (SVM) classifier to map from a given class of structures to their corresponding structural (SCOP) or functional (Gene Ontology) annotation. In particular, we build upon recent work describing various kernels for protein structures, where a kernel is a similarity function that the classifier uses to compare pairs of structures. Results: We describe a kernel that is derived in a straightforward fashion from an existing structural alignment program, MAMMOTH. We find in our benchmark experiments that this kernel significantly out-performs a variety of other kernels, including several previously described kernels. Furthermore, in both benchmarks, classifying structures using MAMMOTH alone does not work as well as using an SVM with the MAMMOTH kernel. Availability: http://noble.gs.washington.edu/proj/3dkernel Contact: noble@gs.washington.edu