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Detecting DNA regulatory motifs by incorporating positional trends in information content

GENOME BIOLOGY 2004年第7期5卷 1-21页

作者： Kechris, KJ van Zwet, E Bickel, PJ Eisen, MB Univ Calif Berkeley Dept Stat Berkeley CA 94720 USA Univ Calif Berkeley Lawrence Berkeley Lab Div Life Sci Dept Genome Sci Berkeley CA 94720 USA Univ Calif Berkeley Dept Mol & Cell Biol Ctr Integrat Genom Berkeley CA 94720 USA

On the basis of the observation that conserved positions in transcription factor binding sites are often clustered together, we propose a simple extension to the model-based motif discovery methods. We assign position-specific prior distributions to the frequency parameters of the model, penalizing deviations from a specified conservation profile. Examples with both simulated and real data show that this extension helps discover motifs as the data become noisier or when there is a competing false motif.

关键词： Prior Distribution Change Point Transcription Factor Binding Site Additional Data File expectation maximization algorithm

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Noise adaptive speech recognition based on sequential noise parameter estimation

Noise adaptive speech recognition based on sequential noise ...

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Workshop on Adaptation Methods for Speech Recognition

作者： Yao, KS Paliwal, KK Nakamura, S ATR Spoken Language Translat Res Labs Kyoto Japan Griffith Univ Sch Microelect Engn Brisbane Qld Australia

In this paper, a noise adaptive speech recognition approach is proposed for recognizing speech which is corrupted by additive non-stationary background noise. The approach sequentially estimates noise parameters, through which a nonlinear parametric function adapts mean vectors of acoustic models. In the estimation process, posterior probability of state sequence given observation sequence and the previously estimated noise parameter sequence is approximated by the normalized joint likelihood of active partial paths and observation sequence given the previously estimated noise parameter sequence. The Viterbi process provides the normalized joint-likelihood. The acoustic models are not required to be trained from clean speech and they can be trained from noisy speech. The approach can be applied to perform continuous speech recognition in presence of non-stationary noise. Experiments conducted on speech contaminated by simulated and real non-stationary noise show that when acoustic models are trained from clean speech, the noise adaptive speech recognition system provides improvements in word accuracy as compared to the normal noise compensation system (which assumes the noise to be stationary) in slowly time-varying noise. When the acoustic models are trained from noisy speech, the noise adaptive speech recognition system is found to be helpful to get improved performance in slowly time-varying noise over a system employing multi-conditional training. (C) 2003 Elsevier B.V. All rights reserved.

关键词： noisy speech recognition non-stationary noise expectation maximization algorithm Kullback proximal algorithm

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Noise adaptive speech recognition based on sequential noise parameter estimation

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SPEECH COMMUNICATION 2004年第1期42卷 5-23页

作者： Yao, KS Paliwal, KK Nakamura, S ATR Spoken Language Translat Res Labs Kyoto Japan Griffith Univ Sch Microelect Engn Brisbane Qld Australia

关键词： noisy speech recognition non-stationary noise expectation maximization algorithm Kullback proximal algorithm

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Alternating minimization multigrid algorithms for transmission tomography

Alternating minimization multigrid algorithms for transmissi...

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Conference on Computational Imaging II

作者： O'Sullivan, JA Benac, J Washington Univ Dept Elect & Syst Engn Elect Syst & Signals Res Lab St Louis MO 63130 USA

ISBN: (纸本)0819452025

The problem of image formation for X-ray transmission tomography is formulated as a statistical inverse problem. The maximum likelihood estimate of the attenuation function is sought. Using convex optimization methods, maximizing the log-likelihood functional is equivalent to a double minimization of I-divergence, one of the minimizations being over the attenuation function. Restricting the minimization over the attenuation function to a coarse grid component forms the basis for a multigrid algorithm that is guaranteed to monotonically decrease the I-divergence at every iteration on every scale.

关键词： expectation maximization algorithm alternating minimization algorithms CT imaging transmission tomography multigrid methods

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A novel Mixture Model Method for identification of differentially expressed genes from DNA microarray data

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BMC BIOINFORMATICS 2004年第1期5卷 201-201页

作者： Najarian, K Zaheri, M Rad, AA Najarian, S Dargahi, J Univ N Carolina Dept Comp Sci Charlotte NC 28223 USA Amirkabir Univ Technol Comp Engn & IT Dept Tehran Iran Concordia Univ CONCAVE Res Ctr Dept Mech & Ind Engn Quebec City PQ Canada

Background: The main goal in analyzing microarray data is to determine the genes that are differentially expressed across two types of tissue samples or samples obtained under two experimental conditions. Mixture model method (MMM hereafter) is a nonparametric statistical method often used for microarray processing applications, but is known to over-fit the data if the number of replicates is small. In addition, the results of the MMM may not be repeatable when dealing with a small number of replicates. In this paper, we propose a new version of MMM to ensure the repeatability of the results in different runs, and reduce the sensitivity of the results on the parameters. Results: The proposed technique is applied to the two different data sets: Leukaemia data set and a data set that examines the effects of low phosphate diet on regular and Hyp mice. In each study, the proposed algorithm successfully selects genes closely related to the disease state that are verified by biological information. Conclusion: The results indicate 100% repeatability in all runs, and exhibit very little sensitivity on the choice of parameters. In addition, the evaluation of the applied method on the Leukaemia data set shows 12% improvement compared to the MMM in detecting the biologically-identified 50 expressed genes by Thomas et al. The results witness to the successful performance of the proposed algorithm in quantitative pathogenesis of diseases and comparative evaluation of treatment methods.

关键词： Bayesian Information Criterion expectation maximization Acute Myeloid Leukaemia Acute Lymphoblastic Leukaemia expectation maximization algorithm

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Prediction of RNA Polymerase Binding Sites Using Purine-Pyrimidine Encoding and Hybrid Learning Methods

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International Journal of Applied Science and Engineering 2004年第2期2卷 177-188页

作者： Cheng-Jian Lin Chun-Cheng Peng and Chi-Yung Lee Department of Computer Science and Information Engineering Chaoyang University of Technology bSchool of Computer Science and Information Systems University of London Department of Electronic Engineering Nan Kai College

Escherichia coli (E. coli) K12 was sequenced in 1997. The 4,639,221-base pair DNA sequence consists of 4288 annotated protein-coding genes, 38 percent of which have no attrib- uted function. One of the major problems in predicting prokaryotic promoters is locating the spacers between the -35 box and -10 box and between the -10 box and transcription start site. In this paper, we use the adopted expectation maximization (EM) algorithm to accurately find the localizations of the promoter regions. A brand new purine-pyrimidine encoding method is pro- posed to reduce the dimensions of the training data. The heavy demand on systems for both computation and memory space can then be avoided through the choice of coding factor. The most representative features are used for training learning vector quantization networks. The simulation results of the proposed coding approach reveal that the precision of promoter predic- tion using the proposed approach is approximately the same as the precision using the traditional encoding method.

关键词： E. coli promoter prediction purine-pyrimidine expectation maximization algorithm learning vector quantization networks.

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System Identification from Multi-Rate Data

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IFAC Proceedings Volumes 2004年第1期37卷 155-160页

作者： R. Bhushan Gopaluni Harigopal Raghavan Sirish L. Shah Department of Chemical & Materials Engineering University of Alberta Edmonton AB CANADA - T6G 2G6

In this paper, we provide a novel iterative identification algorithm for multi-rate sampled data systems. The procedure involves, as a first step, identifying a simple initial model from multi-rate data. Based on this model, the "missing" data points in the slow sampled measurements are estimated following the expectation maximization approach. Using the estimated missing data points and the original data set, a new model is obtained and this procedure is repeated until the models converge. An attractive feature of the proposed method lies in its applicability to irregularly sampled data. An application of the proposed method to an industrial data set is also included.

关键词： identification multi-rate processes expectation maximization algorithm

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Learning mixtures of point distribution models with the EM algorithm

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PATTERN RECOGNITION 2003年第12期36卷 2805-2818页

作者： Al-Shaher, AA Hancock, ER Univ York Dept Comp Sci York YO1 5DD N Yorkshire England

This paper demonstrates how the EM algorithm can be used for learning and matching mixtures of point distribution models. We make two contributions. First, we show how shape-classes can be learned in an unsupervised manner. We present a fast procedure for training point distribution models using the EM algorithm. Rather than estimating the class means and covariance matrices needed to construct the PDM, the method iteratively refines the eigenvectors of the covariance matrix using a gradient ascent technique. Second, we show how recognition by alignment can be realised by fitting a mixture of linear shape deformations. We evaluate the method on the problem of learning the class-structure and recognising Arabic characters. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.

关键词： point distribution models expectation maximization algorithm unsupervised learning alignment shape recognition Arabic character

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Estimating the accuracy of polymerase chain reaction-based tests using endpoint dilution

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BIOMETRICS 2003年第3期59卷 505-511页

作者： Hughes, JP Totten, P Univ Washington Dept Biostat 357232 Seattle WA 98195 USA Univ Washington Dept Med Seattle WA USA

Polymerase chain reaction (PCR)-based tests for various microorganisms or target DNA sequences are generally acknowledged to be highly "sensitive," yet the concept of sensitivity is ill-defined in the literature on these tests. We propose that sensitivity should be expressed as a function of the number of target DNA molecules in the sample (or specificity, when the target number is 0). However, estimating this "sensitivity curve" is problematic, since it is difficult to construct samples with a fixed number of targets. Nonetheless, using serially diluted replicate aliquots of a known concentration of the target DNA sequence, we show that it is possible to disentangle random variations in the number of target DNA molecules from the underlying test sensitivity. We develop parametric, nonparametric, and semiparametric (spline-based) models for the sensitivity curve. The methods are compared on a new test for M. genitalium.

关键词： expectation maximization algorithm endpoint dilution isotonic estimation monotone smoothing spline polymerase chain reaction sensitivity specificity

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Land use/land cover change detection based on canonical transformation

Land use/land cover change detection based on canonical tran...

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Conference on Image Processing and Pattern Recognition in Remote Sensing

作者： Zhang, L Liao, MS Sheng, H Wuhan Univ Natl Lab Informat Engn Surveying Mapping & Remote Wuhan 430079 Hubei Peoples R China

ISBN: (纸本)081944684X

Change detection is a key topic in land use/land cover related studies and significant efforts have been made in the development of methods for change detection. In this article a multivariate analysis method based on canonical transformation is introduced into change detection using multi-temporal remote sensing imageries. Afterwards an automatic unsupervised discriminating technique based on the Bayes-Rule of Minimum Error is employed for changed areas identification in the difference image. Experimental results of a case study using Landsat TM imageries are presented to demonstrate the effectiveness of our method.

关键词： land use/land cover change detection canonical transformation multivariate alteration detection Bayes Rule of Minimum Error expectation maximization algorithm

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