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657
Marginalized kernels between labeled graphs
 Proceedings of the Twentieth International Conference on Machine Learning
, 2003
"... A new kernel function between two labeled graphs is presented. Feature vectors are defined as the counts of label paths produced by random walks on graphs. The kernel computation finally boils down to obtaining the stationary state of a discretetime linear system, thus is efficiently performed by s ..."
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Cited by 144 (14 self)
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A new kernel function between two labeled graphs is presented. Feature vectors are defined as the counts of label paths produced by random walks on graphs. The kernel computation finally boils down to obtaining the stationary state of a discretetime linear system, thus is efficiently performed by solving simultaneous linear equations. Our kernel is based on an infinite dimensional feature space, so it is fundamentally different from other string or tree kernels based on dynamic programming. We will present promising empirical results in classification of chemical compounds. 1 1.
Pfold: RNA secondary structure prediction using stochastic contextfree grammars
 Nucleic Acids Res
, 2003
"... RNA secondary structures are important in many biological processes and efficient structure prediction can give vital directions for experimental investigations. Many available programs for RNA secondary structure prediction only use a single sequence at a time. This may be sufficient in some applic ..."
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Cited by 132 (6 self)
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RNA secondary structures are important in many biological processes and efficient structure prediction can give vital directions for experimental investigations. Many available programs for RNA secondary structure prediction only use a single sequence at a time. This may be sufficient in some applications, but often it is possible to obtain related RNA sequences with conserved secondary structure. These should be included in structural analyses to give improved results. This work presents a practical way of predicting RNA secondary structure that is especially useful when related sequences can be obtained. The method improves a previous algorithm based on an explicit evolutionary model and a probabilistic model of structures. Predictions can be done on a web server at
Diffusion kernels on graphs and other discrete structures
 In Proceedings of the ICML
, 2002
"... The application of kernelbased learning algorithms has, so far, largely been confined to realvalued data and a few special data types, such as strings. In this paper we propose a general method of constructing natural families of kernels over discrete structures, based on the matrix exponentiation ..."
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Cited by 126 (4 self)
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The application of kernelbased learning algorithms has, so far, largely been confined to realvalued data and a few special data types, such as strings. In this paper we propose a general method of constructing natural families of kernels over discrete structures, based on the matrix exponentiation idea. In particular, we focus on generating kernels on graphs, for which we propose a special class of exponential kernels, based on the heat equation, called diffusion kernels, and show that these can be regarded as the discretisation of the familiar Gaussian kernel of Euclidean space.
RNA secondary structure prediction using stochastic contextfree grammars and evolutionary history
, 1999
"... Motivation: Many computerized methods for RNA secondary structure prediction have been developed. Few of these methods, however, employ an evolutionary model, thus relevant information is often left out from the structure determination. This paper introduces a method which incorporates evolutionary ..."
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Cited by 123 (12 self)
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Motivation: Many computerized methods for RNA secondary structure prediction have been developed. Few of these methods, however, employ an evolutionary model, thus relevant information is often left out from the structure determination. This paper introduces a method which incorporates evolutionary history into RNA secondary structure prediction. The method reported here is based on stochastic contextfree grammars (SCFGs) to give a prior probability distribution of structures.
Dynamic Alignment Kernels
 Advances in Large Margin Classifiers
, 1999
"... There is much current interest in kernel methods for classification, regression, PCA, and other linear methods of data analysis. Kernel methods may be particularly valuable for problems in which the input data is not readily described by explicit feature vectors. One such problem is where input data ..."
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Cited by 122 (3 self)
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There is much current interest in kernel methods for classification, regression, PCA, and other linear methods of data analysis. Kernel methods may be particularly valuable for problems in which the input data is not readily described by explicit feature vectors. One such problem is where input data consists of symbolsequences of different lengths, and the relationships between sequences are best captured by dynamic alignment scores. This paper shows that the scores produced by certain dynamic alignment algorithms for sequences are in fact valid kernel functions. This is proved by expressing the alignment scores explicitly as dotproducts. Alignment kernels are potentially applicable to biological sequence data, speech data, and time series data. The kernel construction may be extended from pair HMMs to pair probabilistic contextfree grammars. Introduction: Linear Methods using Kernel Functions 1 1 Introduction: Linear Methods using Kernel Functions In many types of machine learni...
Probabilistic discovery of time series motifs
, 2003
"... Several important time series data mining problems reduce to the core task of finding approximately repeated subsequences in a longer time series. In an earlier work, we formalized the idea of approximately repeated subsequences by introducing the notion of time series motifs. Two limitations of thi ..."
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Cited by 119 (21 self)
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Several important time series data mining problems reduce to the core task of finding approximately repeated subsequences in a longer time series. In an earlier work, we formalized the idea of approximately repeated subsequences by introducing the notion of time series motifs. Two limitations of this work were the poor scalability of the motif discovery algorithm, and the inability to discover motifs in the presence of noise. Here we address these limitations by introducing a novel algorithm inspired by recent advances in the problem of pattern discovery in biosequences. Our algorithm is probabilistic in nature, but as we show empirically and theoretically, it can find time series motifs with very high probability even in the presence of noise or “don’t care ” symbols. Not only is the algorithm fast, but it is an anytime algorithm, producing likely candidate motifs almost immediately, and gradually improving the quality of results over time.
Adaptive Computing on the Grid Using AppLeS
, 2003
"... Ensembles of distributed, heterogeneous resources, also known as Computational Grids are emerging as critical platforms for highperformance and resourceintensive applications. Such platforms provide the potential for applications to aggregate enormous bandwidth, computational power, memory, second ..."
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Cited by 108 (7 self)
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Ensembles of distributed, heterogeneous resources, also known as Computational Grids are emerging as critical platforms for highperformance and resourceintensive applications. Such platforms provide the potential for applications to aggregate enormous bandwidth, computational power, memory, secondary storage, and other resources during a single execution. However, achieving this performance potential in dynamic, heterogeneous environments is challenging. Recent experience with distributed applications indicates that adaptivity is fundamental to achieving application performance in dynamic Grid environments. The AppLeS (Application Level Scheduling) project provides a methodology, application software, and software environments for adaptively scheduling and deploying applications in dynamic, heterogeneous, multiuser Grid environments. In this paper, we discuss the AppLeS project and outline our results.
Combining phylogenetic and hidden Markov models in biosequence analysis
 J. Comput. Biol
, 2004
"... A few models have appeared in recent years that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way the process changes from one site to the next. These models combine phylogenetic models of molecular evolution, which apply to individ ..."
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Cited by 103 (12 self)
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A few models have appeared in recent years that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way the process changes from one site to the next. These models combine phylogenetic models of molecular evolution, which apply to individual sites, and hidden Markov models, which allow for changes from site to site. Besides improving the realism of ordinary phylogenetic models, they are potentially very powerful tools for inference and prediction—for gene finding, for example, or prediction of secondary structure. In this paper, we review progress on combined phylogenetic and hidden Markov models and present some extensions to previous work. Our main result is a simple and efficient method for accommodating higherorder states in the HMM, which allows for contextsensitive models of substitution— that is, models that consider the effects of neighboring bases on the pattern of substitution. We present experimental results indicating that higherorder states, autocorrelated rates, and multiple functional categories all lead to significant improvements in the fit of a combined phylogenetic and hidden Markov model, with the effect of higherorder states being particularly pronounced.
A benchmark of multiple sequence alignment programs upon structural RNAs
 Nucleic Acids Res
, 2005
"... To date, few attempts have been made to benchmark the alignment algorithms upon nucleic acid sequences. Frequently, sophisticated PAM or BLOSUM like models are used to align proteins, yet equivalents are not considered for nucleic acids; instead, rather ad hoc models are generally favoured. Here, we ..."
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Cited by 91 (12 self)
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To date, few attempts have been made to benchmark the alignment algorithms upon nucleic acid sequences. Frequently, sophisticated PAM or BLOSUM like models are used to align proteins, yet equivalents are not considered for nucleic acids; instead, rather ad hoc models are generally favoured. Here, we systematically test the performance of existing alignment algorithms on structural RNAs. This work was aimed at achieving the following goals: (i) to determine conditions where it is appropriate to apply common sequence alignment methods to the structuralRNAalignmentproblem.Thisindicates where and when researchers should consider augmenting the alignment process with auxiliary information, such as secondary structure and (ii) to determine which sequence alignment algorithms perform well under the broadest range of conditions. We find that sequence alignment alone, using the current algorithms, is generally inappropriate,50–60 % sequence identity. Second, we note that the probabilistic method ProAlign and the aging Clustal algorithms generally outperform other sequencebased algorithms, under the broadest range of applications.
Gascuel O. Approximate LikelihoodRatio Test for Branches: A
 Fast, Accurate, and Powerful Alternative. Systematic Biology
"... Abstract.—We revisit statistical tests for branches of evolutionary trees reconstructed upon molecular data. A new, fast, approximate likelihoodratio test (aLRT) for branches is presented here as a competitive alternative to nonparametric bootstrap and Bayesian estimation of branch support. The aLR ..."
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Cited by 86 (4 self)
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Abstract.—We revisit statistical tests for branches of evolutionary trees reconstructed upon molecular data. A new, fast, approximate likelihoodratio test (aLRT) for branches is presented here as a competitive alternative to nonparametric bootstrap and Bayesian estimation of branch support. The aLRT is based on the idea of the conventional LRT, with the null hypothesis corresponding to the assumption that the inferred branch has length 0. We show that the LRT statistic is asymptotically distributed as a maximum of three random variables drawn from the 1 2 1 2 χ 2 0 + χ