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On the use of DNA pooling to estimate haplotype frequencies

GENETIC EPIDEMIOLOGY 2003年第1期24卷 74-82页

作者： Wang, S Kidd, KK Zhao, HY Yale Univ Sch Med Dept Epidemiol & Publ Hlth New Haven CT 06520 USA Yale Univ Sch Med Dept Genet New Haven CT 06510 USA

Genome-wide association studies may be necessary to identify genes underlying certain complex diseases. Because such studies can be extremely expensive, DNA pooling has been introduced, as it may greatly reduce the genotyping burden. Parallel to DNA pooling developments, the importance of haplotypes in genetic studies has been amply demonstrated in the literature. However, DNA pooling of a large number of samples may lose haplotype information among tightly linked genetic markers. Here, we examine the cost-effectiveness of DNA pooling in the estimation of haplotype frequencies from population data. When the maximum likelihood estimates of haplotype frequencies are obtained from pooled samples, we compare the overall cost of the study, including both DNA collection and marker genotyping, between the individual genotyping strategy and the DNA pooling strategy. We find that the DNA pooling of two individuals can be more cost-effective than individual genotypings, especially when a large number of haplotype systems are studied.

关键词： haplotypes genotypes DNA pooling expectation-maximization algorithm

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An EM algorithm for wavelet-based image restoration

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2003年第8期12卷 906-916页

作者： Figueiredo, MAT Nowak, RD Inst Super Tecn Inst Telecommun P-1049001 Lisbon Portugal Inst Super Tecn Dept Elect & Comp Engn P-1049001 Lisbon Portugal Rice Univ Dept Elect & Comp Engn Houston TX 77001 USA

This paper introduces an expectation-maximization (EM) algorithm for image restoration (deconvolution) based on a penalized likelihood formulated in the wavelet domain. Regularization is achieved by promoting a reconstruction with low-complexity, expressed in the wavelet coefficients, taking advantage of the well known sparsity of wavelet representations. Previous works have investigated wavelet-based restoration but, except for certain special cases, the resulting criteria are solved approximately or require demanding optimization methods. The EM algorithm herein proposed combines the efficient image representation offered by the discrete wavelet transform (DWT) with the diagonalization of the convolution operator obtained in the Fourier domain. Thus, it is a general-purpose approach to wavelet-based image restoration with computational complexity comparable to that of standard wavelet denoising schemes or of frequency domain deconvolution methods. The algorithm alternates between an E-step based on the fast Fourier transform (FFT) and a DWT-based M-step, resulting in an efficient iterative process requiring O(N log N) operations per iteration. The convergence behavior of the algorithm is investigated, and it is shown that under mild conditions the algorithm converges to a globally optimal restoration. Moreover, our new approach performs competitively with, in some cases better than, the best existing methods in benchmark tests.

关键词： Bayesian estimation expectation-maximization algorithm image deconvolution image restoration penalized maximum likelihood wavelets

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A theoretical assessment of image analysis: Statistical vs structural approaches

A theoretical assessment of image analysis: Statistical vs s...

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Medical Imaging 2003 Conference

作者： Lei, TH Udupa, JK Univ Penn Dept Radiol MIPG Philadelphia PA 19104 USA

ISBN: (纸本)0819448338

Statistical and structural methods axe two major approaches commonly used in image analysis and have demonstrated considerable success. The former is based on statistical properties and stochastic models of the image and the latter utilizes geometric and topological models. In this study, Markov random field (MRF) theory / model based image segmentation and Fuzzy Connectedness (FC) theory / Fuzzy connected object delineation are chosen as the representatives for these two approaches, respectively. The comparative study is focused on their theoretical foundations and main operative procedures. The MRF is defined on a lattice and the associated neighborhood system and is based on the Markov property. The FC method is defined on a fuzzy digital space and is based on fuzzy relations. Locally, MRF is characterized by potentials of cliques, and FC is described by fuzzy adjacency and affinity relations. Globally, MRF is characterized by Gibbs distribution, and FC is described by fuzzy connectedness. The task of MRF model based image segmentation is to seek a realization of the embedded MRF through a two-level operation: partitioning and labeling. The task of FC object delineation is to extract a fuzzy object from a given scene, through a two-step operation: recognition and delineation. Theoretical foundations which underly statistical and structural approaches and the principles of the main operative procedures in image segmentation by these two approaches demonstrate more similarities than differences between them. Two approaches can also complement each other, particularly in seed selection, scale formation, affinity and object membership function design for FC and neighbor set selection and clique potential design for MRF.

关键词： Markov random field neighborhood system clique potential fuzzy connectedness adjacency affinity image segmentation expectation-maximization algorithm Iterated Conditional Mode dynamic programming

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A simple channel estimator for OFDM with transmit diversity in rapid fading channels

A simple channel estimator for OFDM with transmit diversity ...

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14th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications

作者： Shan, SW Luo, HW Song, WT Shanghai Jiao Tong Univ Dept Elect Engn Shanghai 200030 Peoples R China

ISBN: (纸本)7505350668

This paper presents a simple channel estimator for orthogonal frequency division multiplexing (OFDM) systems with transmit diversity in rapid fading channels. The channels at the training bauds are estimated using the EM (expectation-maximization) algorithm. while the channels at the data bauds are estimated based on the method that the time-varying channel can be modelled as the linear combination of several time-invariant "Doppler channels". Computer simulations show that this estimator outperforms the decision-directed tracking in rapid fading channels and the performance of this method call be improved by iteration.

关键词： channel estimation OFDM modulation fading channels time-varying channels Doppler effect channel estimator OFDM transmit diversity fading channel orthogonal frequency division multiplexing training baud channel expectation-maximization algorithm data baud channel time-varying channel linear combination time-invariant Doppler channel decision-directed tracking iteration method

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Unsupervised learning of finite mixture models

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2002年第3期24卷 381-396页

作者： Figueiredo, MAT Jain, AK Inst Super Tecn Inst Telecommun P-1049001 Lisbon Portugal Inst Super Tecn Dept Elect & Comp Engn P-1049001 Lisbon Portugal Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

This paper proposes an unsupervised algorithm for learning a finite mixture model from multivariate data. The adjective "unsupervised" is justified by two properties of the algorithm: 1) it is capable of selecting the number of components and 2) unlike the standard expectation-maximization (EM) algorithm, it does not require careful initialization. The proposed method also avoids another drawback of EM for mixture fitting: the possibility of convergence toward a singular estimate at the boundary of the parameter space. The novelty of our approach is that we do not use a model selection criterion to choose one among a set of preestimated candidate models;instead, we seamlessly integrate estimation and model selection in a single algorithm. Our technique can be applied to any type of parametric mixture model for which it is possible to write an EM algorithm;in this paper, we illustrate it with experiments involving Gaussian mixtures. These experiments testify for the good performance of our approach.

关键词： finite mixtures unsupervised learning model selection minimum message length criterion Bayesian methods expectation-maximization algorithm clustering

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Genomewide motif identification using a dictionary model

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PROCEEDINGS OF THE IEEE 2002年第11期90卷 1803-1810页

作者： Sabatti, C Lange, K Univ Calif Los Angeles Dept Human Genet Los Angeles CA 90095 USA Univ Calif Los Angeles Biomath Dept Los Angeles CA 90095 USA Univ Calif Los Angeles Dept Stat Los Angeles CA 90095 USA

This paper surveys and extends models and algorithms for identifying binding sites in noncoding regions of DNA. Binding sites control the transcription of genes into messenger RNA in preparation for translation into proteins. The base sequence of most binding sites is not entirely fixed, with the different permitted spellings collectively constituting a "motif." After summarizing the underlying biological issues, we review three different models for binding site identification. Each model was developed with a different type of dataset as reference. We then present a unified model that borrows from the previous ones and integrates their main features. In our unified model, one can identify motifs and their unknown positions along a sequence. One can also fit the model to data using maximum likelihood and maximum a posteriori algorithms. These algorithms rely on recursive formulas and the maximization/minorization principle. Finally, we conclude with a prospectus of future data analyses and theoretical research.

关键词： expectation-maximization algorithm genomic sequence maximum a posteriori text segmentation

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Unsupervised change-detection methods for remote-sensing images

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OPTICAL ENGINEERING 2002年第12期41卷 3288-3297页

作者： Melgani, F Moser, G Serpico, SB Univ Trent Dept Informat & Commun Technol I-38050 Trento Italy Univ Genoa Dept Biophys & Elect Engn I-16145 Genoa Italy

An unsupervised change detection problem can be viewed as a classification problem with only two classes corresponding to the change and no-change areas, respectively. Thanks to its simplicity, image differencing is a widely used approach to change detection. It is based on the idea of generating a difference image that represents the modulus of the spectral change vector associated with each pixel in the study area. To separate the "change" and "no-change" classes in the difference image, a simple thresholding-based procedure can be applied. However, the selection of the best threshold value is not a trivial problem. We investigate and compare several simple thresholding methods. The combination of the expectation-maximization algorithm with a thresholding method is also performed for the purpose of achieving a better estimate of the optimal threshold value. As an experimental investigation, a study area damaged by a forest fire is considered. Two Landsat TM images of the area acquired before and after the event are utilized to detect the burnt zones and to assess and compare the mentioned unsupervised change-detection methods. (C) 2002 Society of Photo-Optical Instrumentation Engineers.

关键词： remote sensing pattern classification change detection thresholding expectation-maximization algorithm

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An adaptive semiparametric and context-based approach to unsupervised change detection in multitemporal remote-sensing images

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2002年第4期11卷 452-466页

作者： Bruzzone, L Prieto, DF Univ Trent Dept Informat & Commun Technol I-38050 Trento Italy

In this paper, a novel automatic approach to the unsupervised identification of changes in multitemporal remote-sensing images is proposed. This approach, unlike classical ones, is based on the formulation of the unsupervised change-detection problem in terms of the Bayesian decision theory. In this context, an adaptive semiparametric technique for the unsupervised estimation of the statistical terms associated with the gray levels of changed and unchanged pixels in a difference image is presented. Such a technique exploits the effectivenesses of two theoretically well-founded estimation procedures: the reduced Parzen estimate (RPE) procedure and the expectation-maximization (EM) algorithm. Then, thanks to the resulting estimates and to a Markov Random Field (MRF) approach used to model the spatial-contextual information contained in the multitemporal images considered, a change detection map is generated. The adaptive semiparametric nature of the proposed technique allows its application to different kinds of remote-sensing images. Experimental results, obtained on two sets of multitemporal remote-sensing images acquired by two different sensors, confirm the validity of the proposed approach.

关键词： adaptive semiparametric estimation Bayes theory change detection expectation-maximization algorithm multitemporal images reduced Parzen estimate remote sensing

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Image coding using transform vector quantization with training set synthesis

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SIGNAL PROCESSING 2002年第11期82卷 1649-1663页

作者： Comaniciu, D Grisel, R Siemens Corp Res Real Time Vis & Modeling Dept Princeton NJ 08540 USA CNRS EP 0092 CPE LISA F-69616 Villeurbanne France

A new paradigm that combines data modeling and vector quantization in an effective coding technique is presented. We fit a statistical model to the input data and use the best fit parameters to synthesize training vector sets with statistics similar to the input. By knowing the best-fit parameters, the decoder can synthesize the same training sets, while identical codebooks are obtained at both encoder and decoder based on the same codebook generation procedure. As a result, complete codebook adaptation is achieved with a very small increase in the bit rate. The implementation of the new technique in the transform domain produced competitive results when compared to other methods relying on vector quantization and transform coding. In particular, the image Lena was coded at 0.28 bits/pixel with a peak signal-to-noise ratio of 32.51 dB. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： image coding training set synthesis transform vector quantization expectation-maximization algorithm

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Principal component analysis of dissolution data with missing elements

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INTERNATIONAL JOURNAL OF PHARMACEUTICS 2002年第1-2期234卷 169-178页

作者： Adams, E Walczak, B Vervaet, C Risha, PG Massart, DL Free Univ Brussels Inst Pharmaceut B-1090 Brussels Belgium Silesian Univ Inst Chem PL-40006 Katowice Poland State Univ Ghent Lab Pharmaceut Technol B-9000 Ghent Belgium Muhimbili Univ Coll Hlth Sci Dar Es Salaam Tanzania

The use of principal component analysis (PCA) for incomplete dissolution data sets is examined. The PC space is constructed using a reference set and the test set is projected in that space. Several cases such as a reference set with missing data, an incomplete test set and both sets measured at different time points, are discussed using two examples: one simulation and one obtained from the pharmaceutical practice. From the many possibilities to deal with missing data, the expectation-maximization algorithm in combination with PCA was chosen. The influence on the similarity or f(2) factor is examined too. The sampling with replacement or bootstrap technique, which can be used to obtain confidence limits, can also be used when missing data are present in one of the data sets. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： dissolution missing data expectation-maximization algorithm principal component analysis

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