Machine Learning
Automata: Theoretic Aspects of Formal Power Series
Automata: Theoretic Aspects of Formal Power Series
Text classification using string kernels
The Journal of Machine Learning Research
Rational Kernels: Theory and Algorithms
The Journal of Machine Learning Research
Fast String Kernels using Inexact Matching for Protein Sequences
The Journal of Machine Learning Research
Moment Kernels for Regular Distributions
Machine Learning
Kernel methods for predicting protein--protein interactions
Bioinformatics
OpenFst: a general and efficient weighted finite-state transducer library
CIAA'07 Proceedings of the 12th international conference on Implementation and application of automata
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Learning with Weighted Transducers
Proceedings of the 2009 conference on Finite-State Methods and Natural Language Processing: Post-proceedings of the 7th International Workshop FSMNLP 2008
Large-scale training of SVMs with automata kernels
CIAA'10 Proceedings of the 15th international conference on Implementation and application of automata
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The problem of identifying the minimal gene set required to sustain life is of crucial importance in understanding cellular mechanisms and designing therapeutic drugs. This work describes several kernel-based solutions for predicting essential genes that outperform existing models while using less training data. Our first solution is based on a semi-manually designed kernel derived from the Pfam database, which includes several Pfam domains. We then present novel and general domain-based sequence kernels that capture sequence similarity with respect to several domains made of large sets of protein sequences. We show how to deal with the large size of the problem -- several thousands of domains with individual domains sometimes containing thousands of sequences -- by representing and efficiently computing these kernels using automata. We report results of extensive experiments demonstrating that they compare favorably with the Pfam kernel in predicting protein essentiality, while requiring no manual tuning.