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A Statistical Framework to Predict Functional Non-Coding Regions in the Human Genome Through Integrated Analysis of Annotation Data

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SCIENTIFIC REPORTS 2015年第1期5卷 10576-10576页

作者： Lu, Qiongshi Hu, Yiming Sun, Jiehuan Cheng, Yuwei Cheung, Kei-Hoi Zhao, Hongyu Yale Univ Sch Publ Hlth Dept Biostat New Haven CT 06520 USA Yale Univ Program Computat Biol & Bioinformat New Haven CT USA Yale Univ Sch Med Yale Ctr Med Informat New Haven CT USA Yale Univ Sch Med Dept Emergency Med New Haven CT USA VA Connecticut Healthcare Syst West Haven CT USA

Identifying functional regions in the human genome is a major goal in human genetics. Great efforts have been made to functionally annotate the human genome either through computational predictions, such as genomic conservation, or high-throughput experiments, such as the ENCODE project. These efforts have resulted in a rich collection of functional annotation data of diverse types that need to be jointly analyzed for integrated interpretation and annotation. Here we present GenoCanyon, a whole-genome annotation method that performs unsupervised statistical learning using 22 computational and experimental annotations thereby inferring the functional potential of each position in the human genome. With GenoCanyon, we are able to predict many of the known functional regions. The ability of predicting functional regions as well as its generalizable statistical framework makes GenoCanyon a unique and powerful tool for whole-genome annotation. The GenoCanyon web server is available at http://***

关键词： HUMAN genome NON-coding DNA NON-coding RNA GENE expression expectation-maximization algorithms

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Automatic design of an effective image filter based on an evolutionary algorithm for venous analysis

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NETWORK MODELING AND ANALYSIS IN HEALTH INFORMATICS AND BIOINFORMATICS 2016年第1期5卷 1-1页

作者： Kashihara, Koji Iwase, Masashi Univ Tokushima Inst Technol & Sci 2-1 Minamijyousanjima Tokushima 7708506 Japan Univ Tokushima Dept Informat Sci & Intelligent Syst 2-1 Minamijyousanjima Tokushima 7708506 Japan

Medical doctors and clinical technologists operate specific, complicated diagnostic systems to assess venous diseases. Instead of using such expensive equipment, low-cost infrared cameras can capture vein images noninvasively and simply. However, the recorded image has a possibility to result in low contrast and a low signalto-noise (S/N) ratio. An effective image filtering method to estimate venous changes will solve this problem and enable the early detection of disease. For this study, a novel filtering method based on the genetic algorithm (GA) with the expectation-maximization algorithm was proposed for the visualization of vein shapes;its effectiveness was evaluated by images acquired from a near-infrared (780 nm) camera. The novel filter was able to be automatically designed by the GA to improve the worse S/N ratio of vein images, with an unknown correct answer image. If the proposed filtering method is incorporated into e-healthcare applications, it could be widely distributed through smartphones or tablets and facilitate finding abnormal veins at an early stage.

关键词： expectation-maximization algorithms Gaussian mixture model Genetic algorithms Filters Infrared image sensors

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On Ryde/spl acute/n's EM algorithm for estimating MMPPs

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IEEE Signal Processing Letters 2006年第6期13卷 373-376页

作者： W.J.J. Roberts Y. Ephraim E. Dieguez Atlantic Coast Technologies Inc. Silver Spring MD USA Department of Electrical and Computer Engineering George Mason University Fairfax VA USA

Two aspects of Ryden's expectation-maximization algorithm for estimating the parameter of a Markov modulated Poisson process are addressed. First, a scaling procedure is developed for the forward-backward recursions that circumvents the need for customized floating-point software. Second, evaluation of integrals of matrix exponentials is facilitated by applying a result due to Van Loan. For an MMPP of order four, a speedup of over two orders of magnitude was observed.

关键词： Parameter estimation Signal processing algorithms Maximum likelihood estimation Application software State estimation expectation-maximization algorithms Computer networks Finance Recursive estimation Control theory

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High-breakdown estimation of multivariate mean and covariance with missing observations

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BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY 2002年第Pt 2期55卷 317-335页

作者： Cheng, TC Victoria-Feser, MP Univ Geneva Fac Psychol & Educ CH-1211 Geneva 4 Switzerland Natl Chengchi Univ Dept Stat Taipei Taiwan

We consider the problem of outliers in incomplete multivariate data when the aim is to estimate a measure of mean and covariance, as is the case, for example, in factor analysis. The ER algorithm of Little and Smith which combines the EM algorithm for missing data and a robust estimation step based on an M-estimator could be used in such a situation. However, the ER algorithm as originally proposed can fail to be robust in some cases, especially in high dimensions. We propose here two alternatives to avoid the problem. One is to combine a small modification of the ER algorithm with a so-called high-breakdown estimator as the starting point for the iterative procedure, and the other is to base the estimation step of the ER algorithm on a high-breakdown estimator. Among the high-breakdown estimators which are actually built to keep their robustness properties even if the number of variables is relatively large, we consider here the minimum covariance determinant estimator and the t-biweight S-estimator. Simulated and real data are used to compare and illustrate the different procedures.

关键词： PSYCHOLOGY expectation-maximization algorithms

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Level Set Method for Positron Emission Tomography

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INTERNATIONAL JOURNAL OF BIOMEDICAL IMAGING 2007年第unknown期2007卷 1-15页

作者： Chan, Tony F. Li, Hongwei Lysaker, Marius Tai, Xue-Cheng Univ Calif Los Angeles Dept Math 405 Hilgard Ave Los Angeles CA 90095 USA Univ Bergen Ctr Integrated Petr Res N-5007 Bergen Norway Simula Res Lab AS Dept Sci Comp N-1325 Lysaker Norway Henan Univ Dept Math & Syst Sci Kaifeng 475001 Peoples R China Univ Bergen Dept Math N-5009 Bergen Norway

In positron emission tomography (PET), a radioactive compound is injected into the body to promote a tissue-dependent emission rate. expectation maximization (EM) reconstruction algorithms are iterative techniques which estimate the concentration coefficients that provide the best fitted solution, for example, a maximum likelihood estimate. In this paper, we combine the EM algorithm with a level set approach. The level set method is used to capture the coarse scale information and the discontinuities of the concentration coefficients. An intrinsic advantage of the level set formulation is that anatomical information can be efficiently incorporated and used in an easy and natural way. We utilize a multiple level set formulation to represent the geometry of the objects in the scene. The proposed algorithm can be applied to any PET configuration, without major modifications. Copyright (C) 2007 Tony F. Chan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

关键词： EMISSION tomography LEVEL set methods expectation-maximization algorithms MEDICINE -- Mathematics DIAGNOSTIC imaging BIOMEDICAL engineering -- Research

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A Ground-Truth Fusion Method for Image Segmentation Evaluation

A Ground-Truth Fusion Method for Image Segmentation Evaluati...

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IEEE Southwest Symposium on Image Analysis and Interpretation

作者： Sree Ramya S. P. Malladi Sundaresh Ram Jeffrey J. Rodríguez Department of Electrical and Computer Engineering University of Arizona Tucson AZ USA School of Electrical and Computer Engineering Cornell University Ithaca NY USA

Image segmentation evaluation is popularly categorized into two different approaches based on whether the evaluation uses a human expert's manual segmentation as a reference or not. When comparing automated segmentation against manual segmentation, also referred to as the ground-truth segmentation, multiple ground-truths are usually available. Much research has been done on analysis of segmentation algorithms and performance metrics, but very little study has been done on analyzing techniques for ground-truth fusion from multiple ground-truth segmentations. We propose a hybrid ground-truth fusion technique for image segmentation evaluation and compare it with other existing ground-truth fusion methods on a data set having multiple ground-truths at various coarseness levels. Qualitative and quantitative results show that the proposed method provides improved segmentation evaluation performance.

关键词： Image segmentation Manuals Indexes Signal processing algorithms expectation-maximization algorithms Q measurement

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/spl alpha/-EM algorithm and /spl alpha/-ICA learning based upon extended logarithmic information measures

/spl alpha/-EM algorithm and /spl alpha/-ICA learning based ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Y. Mtsuyama T. Nimoto N. Katsumata Y. Suzuki S. Furukawa Waseda Daigaku Shinjuku-ku Tokyo JP Department of Electrical Electronics and Computer Engineering Waseda University Tokyo Japan

The /spl alpha/-logarithm extends the logarithm as the special case of /spl alpha/=-1. Usage of /spl alpha/-related information measures based upon this extended logarithm is expected to be effective to speedup of convergence, i.e., on the improvement of learning aptitude. In this paper, two typical cases are investigated. One is the /spl alpha/-EM algorithm (/spl alpha/-expectation-maximization algorithm) which is derived from the /spl alpha/-log-likelihood ratio. The other is the /spl alpha/-ICA (/spl alpha/-independent component analysis) which is formulated as minimizing the /spl alpha/-mutual information. In the derivation of both algorithms, the /spl alpha/-divergence plays the main role. For the /spl alpha/-EM algorithm, the reason for the speedup is explained using Hessian and Jacobian matrices for learning. For the /spl alpha/-ICA learning, methods of exploiting the past and future information are presented. Examples are shown on single-loop /spl alpha/-EM and sample-based /spl alpha/-ICA. In all cases, effective speedups are observed. Thus, this paper's examples together with formerly reported ones are evidences that the speed improvement by the /spl alpha/-logarithm is a general property beyond individual problems.

关键词： Velocity measurement Independent component analysis Jacobian matrices Convergence Information analysis Entropy Mutual information expectation-maximization algorithms Minimization methods Equations

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Maximum-likelihood compensation of zero-memory nonlinearities in speech signals

Maximum-likelihood compensation of zero-memory nonlinearitie...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： R.W. Morris M.A. Clements Center for Signal & Image Processing Georgia Institute of Technology Atlanta GA USA

An algorithm to blindly compensate zero-memory nonlinear distortions of speech waveforms is derived and analyzed. This method finds a maximum-likelihood estimate of the distortion without a priori knowledge of the microphone characteristics by using the expectation-maximization algorithm. The autoregressive signal model coefficients are solved jointly with the nonlinearity estimate created by an extended Kalman filter. Also, a new family of nonlinear functions is developed for use with this algorithm, although the method can estimate the shape of any parametric zero-memory nonlinearity. These nonlinear distortions can degrade the speech recognition rates, yet lower the perceptual quality only slightly. The compensation algorithm improves automatic speech recognition of distorted speech for a variety of such nonlinearities.

关键词： Nonlinear distortion Maximum likelihood estimation Speech analysis Algorithm design and analysis Microphones expectation-maximization algorithms Shape Degradation Speech recognition Automatic speech recognition

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A comparison of clustering fully polarimetric SAR images using SEM algorithm and G0P mixture modelwith different initializations

A comparison of clustering fully polarimetric SAR images usi...

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International Conference on Pattern Recognition

作者： Michelle M. Horta Nelson D. A. Mascarenhas Alejandro C. Frery Physics Institute of São Carlos University of São Paulo Sao Paulo Brazil Computer Department Federal University of São Carlos Sao Paulo Brazil Computer Institute Federal University of Alagoas Maceio Alagoas Brazil

ISBN: (纸本)9781424421749

This paper presents a comparison between two types of initializations for multilook polarimetric SAR image segmentation: a random partition and a sample quantile partition. These are the inputs of a stochastic expectation-maximization algorithm that uses a mixture of G 0 P distributions to describe the data. The parameters are unknown, and estimated by the moments method. The G 0 P law is able to describe different type of targets, like urban areas, vegetation and pasture. The experimental results on real PolSAR data are reported, showing that the use of G 0 P model with quantile partition inicialization provide good segmentation results with few iterations.

关键词： Clustering algorithms Covariance matrix Physics computing Image segmentation Convergence Polarization Speckle Stochastic processes expectation-maximization algorithms Urban areas

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Blind separation of non-stationary and non-Gaussian independent sources

Blind separation of non-stationary and non-Gaussian independ...

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IEEE Convention of the Electrical and ELectronic Engineers in Israel

作者： K. Todros J. Tabrikian Department of Electrical and Computer Engineering Ben-Gurion University of the Negev Beersheba Israel

In this paper, the problem of blind separation of an instantaneous mixture of independent sources by exploiting their nonstationarity and/or nonGaussianity is addressed. We show that nonstationarity and nonGaussianity can be exploited by modeling the distribution of the sources using Gaussian mixture model. The maximum likelihood estimator is utilized in order to derive two novel source separation techniques. Both methods are based on estimation of the sensor distribution parameters via the expectation-maximization algorithm for GMM parameter estimation. In the first method, the separation matrix is estimated by applying simultaneous joint diagonalization of the estimated GMM covariance matrices. In the second proposed method the separation matrix is estimated by applying singular value decomposition of a weighted sum of the estimated GMM covariance matrices. The performance of the two proposed methods is evaluated and compared to existing blind source separation techniques. The results show the superior performance of the proposed methods in terms of interference-to-signal ratio.

关键词： Covariance matrix Maximum likelihood estimation Source separation Parameter estimation Matrix decomposition Vectors Sensor arrays Singular value decomposition Blind source separation expectation-maximization algorithms

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