The nature of statistical learning theory
The nature of statistical learning theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Inverse Parametric Sequence Alignment
COCOON '02 Proceedings of the 8th Annual International Conference on Computing and Combinatorics
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
CONTRAlign: discriminative training for protein sequence alignment
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
Simple and fast inverse alignment
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
Training structural SVMs when exact inference is intractable
Proceedings of the 25th international conference on Machine learning
Training structural svms with kernels using sampled cuts
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Scoring Schemes for Sequence Alignment from Partial Examples
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Cutting-plane training of structural SVMs
Machine Learning
Inverse sequence alignment from partial examples
WABI'07 Proceedings of the 7th international conference on Algorithms in Bioinformatics
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Sequence to structure alignment is an important step in homology modeling of protein structures. Incorporation of features like secondary structure, solvent accessibility, or evolutionary information improve sequence to structure alignment accuracy, but conventional generative estimation techniques for alignment models impose independence assumptions that make these features difficult to include in a principled way. In this paper, we overcome this problem using a Support Vector Machine (SVM) method that provides a well-founded way of estimating complex alignment models with hundred-thousands of parameters. Furthermore, we show that the method can be trained using a variety of loss functions. In a rigorous empirical evaluation, the SVM algorithm outperforms the generative alignment method SSALN, a highly accurate generative alignment model that incorporates structural information. The alignment model learned by the SVM aligns 47% of the residues correctly and aligns over 70% of the residues within a shift of 4 positions.