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- ArticleAugust 2005
New kernels for protein structural motif discovery and function classification
ICML '05: Proceedings of the 22nd international conference on Machine learningAugust 2005, Pages 940–947https://doi.org/10.1145/1102351.1102470We present new, general-purpose kernels for protein structure analysis, and describe how to apply them to structural motif discovery and function classification. Experiments show that our new methods are faster than conventional techniques, are capable ...
- ArticleAugust 2005
Large scale genomic sequence SVM classifiers
ICML '05: Proceedings of the 22nd international conference on Machine learningAugust 2005, Pages 848–855https://doi.org/10.1145/1102351.1102458In genomic sequence analysis tasks like splice site recognition or promoter identification, large amounts of training sequences are available, and indeed needed to achieve sufficiently high classification performances. In this work we study two recently ...
- ArticleAugust 2005
Active learning for sampling in time-series experiments with application to gene expression analysis
ICML '05: Proceedings of the 22nd international conference on Machine learningAugust 2005, Pages 832–839https://doi.org/10.1145/1102351.1102456Many time-series experiments seek to estimate some signal as a continuous function of time. In this paper, we address the sampling problem for such experiments: determining which time-points ought to be sampled in order to minimize the cost of data ...
- ArticleAugust 2005
Unsupervised evidence integration
ICML '05: Proceedings of the 22nd international conference on Machine learningAugust 2005, Pages 521–528https://doi.org/10.1145/1102351.1102417Many biological propositions can be supported by a variety of different types of evidence. It is often useful to collect together large numbers of such propositions, together with the evidence supporting them, into databases to be used in other ...
- ArticleAugust 2005
Predicting protein folds with structural repeats using a chain graph model
ICML '05: Proceedings of the 22nd international conference on Machine learningAugust 2005, Pages 513–520https://doi.org/10.1145/1102351.1102416Protein fold recognition is a key step towards inferring the tertiary structures from amino-acid sequences. Complex folds such as those consisting of interacting structural repeats are prevalent in proteins involved in a wide spectrum of biological ...
- ArticleAugust 2005
Multi-class protein fold recognition using adaptive codes
ICML '05: Proceedings of the 22nd international conference on Machine learningAugust 2005, Pages 329–336https://doi.org/10.1145/1102351.1102393We develop a novel multi-class classification method based on output codes for the problem of classifying a sequence of amino acids into one of many known protein structural classes, called folds. Our method learns relative weights between one-vs-all ...
- ArticleAugust 2005
Learning to compete, compromise, and cooperate in repeated general-sum games
ICML '05: Proceedings of the 22nd international conference on Machine learningAugust 2005, Pages 161–168https://doi.org/10.1145/1102351.1102372Learning algorithms often obtain relatively low average payoffs in repeated general-sum games between other learning agents due to a focus on myopic best-response and one-shot Nash equilibrium (NE) strategies. A less myopic approach places focus on NEs ...