Algorithms for approximate string matching
Information and Control
An O(NP) sequence comparison algorithm
Information Processing Letters
Fast text searching: allowing errors
Communications of the ACM
The nature of statistical learning theory
The nature of statistical learning theory
An extension of Ukkonen's enhanced dynamic programming ASM algorithm
ACM Transactions on Information Systems (TOIS)
Detecting non-adjoining correlations with signals in DNA
RECOMB '98 Proceedings of the second annual international conference on Computational molecular biology
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
Translation Initiation Sites Prediction with Mixture Gaussian Models in Human cDNA Sequences
IEEE Transactions on Knowledge and Data Engineering
Multiple Instance Learning Allows MHC Class II Epitope Predictions Across Alleles
WABI '08 Proceedings of the 8th international workshop on Algorithms in Bioinformatics
Learning state machine-based string edit kernels
Pattern Recognition
High efficiency on prediction of translation initiation site (TIS) of RefSeq sequences
BSB'07 Proceedings of the 2nd Brazilian conference on Advances in bioinformatics and computational biology
Transactions on Computational Systems Biology II
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The prediction of translation initiation sites (TISs) in eukaryotic mRNAs has been a challenging problem in computational molecular biology. In this paper, we present a new algorithm to recognize TISs with a very high accuracy. Our algorithm includes two novel ideas. First, we introduce a class of new sequence-similarity kernels based on string edit, called the edit kernels, for use with support vector machines (SVMs) in a discriminative approach to predict TISs. The edit kernels are simple and have significant biological and probabilistic interpretations. Second, we convert the region of an input mRNA sequence downstream to a putative TIS into an amino acid sequence before applying SVMs to avoid the high redundancy in the genetic code. The algorithm has been implemented and tested on previously published data. Our experimental results on real mRNA data show that both ideas improve the prediction accuracy greatly and our method performs significantly better than those based on neural networks and SVMs with polynomial kernels or Salzberg kernel.