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The gene normalization task in BioCreative III

BMC BIOINFORMATICS 2011年第s8期12卷 S2-S2页

作者： Lu, Zhiyong Kao, Hung-Yu Wei, Chih-Hsuan Huang, Minlie Liu, Jingchen Kuo, Cheng-Ju Hsu, Chun-Nan Tsai, Richard Tzong-Han Dai, Hong-Jie Okazaki, Naoaki Cho, Han-Cheol Gerner, Martin Solt, Illes Agarwal, Shashank Liu, Feifan Vishnyakova, Dina Ruch, Patrick Romacker, Martin Rinaldi, Fabio Bhattacharya, Sanmitra Srinivasan, Padmini Liu, Hongfang Torii, Manabu Matos, Sergio Campos, David Verspoor, Karin Livingston, Kevin M. Wilbur, W. John Natl Lib Med Natl Ctr Biotechnol Informat Bethesda MD 20894 USA Natl Cheng Kung Univ Dept Comp Sci & Informat Engn Tainan 70101 Taiwan Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Acad Sinica Inst Informat Sci Taipei 115 Taiwan Univ So Calif Inst Informat Sci Marina Del Rey CA 90292 USA Yuan Ze Univ Dept Comp Sci & Engn Chungli Taiwan Natl Tsing Hua Univ Dept Comp Sci Hsinchu 30043 Taiwan Univ Tokyo Interfac Initiat Informat Studies Tokyo 1138654 Japan Univ Tokyo Grad Sch Informat Sci & Technol Tokyo 1138654 Japan Univ Manchester Fac Life Sci Manchester M13 9PT Lancs England Budapest Univ Technol & Econ Dept Telecommun & Media Informat H-1117 Budapest Hungary Univ Wisconsin Milwaukee WI 53201 USA Univ Geneva Div Med Informat Sci BiTem Grp CH-1211 Geneva 4 Switzerland Univ Appl Sci Dept Informat Sci BiTeM Grp Geneva Switzerland Novartis AG NITAS TMS Text Min Serv Basel Switzerland Univ Zurich Inst Computat Linguist Zurich Switzerland Univ Iowa Dept Comp Sci Iowa City IA 52242 USA Mayo Clin Coll Med Dept Hlth Sci Res Rochester MN 55905 USA Georgetown Univ Med Ctr Lab Text Intelligence Biomed Washington DC 20057 USA Univ Aveiro DETI IEETA P-3810193 Aveiro Portugal Univ Colorado Sch Med Ctr Computat Pharmacol Aurora CO USA

Background: We report the Gene Normalization (GN) challenge in BioCreative III where participating teams were asked to return a ranked list of identifiers of the genes detected in full-text articles. For training, 32 fully and 500 partially annotated articles were prepared. A total of 507 articles were selected as the test set. Due to the high annotation cost, it was not feasible to obtain gold-standard human annotations for all test articles. Instead, we developed an expectation maximization (EM) algorithm approach for choosing a small number of test articles for manual annotation that were most capable of differentiating team performance. Moreover, the same algorithm was subsequently used for inferring ground truth based solely on team submissions. We report team performance on both gold standard and inferred ground truth using a newly proposed metric called Threshold Average Precision (TAP-k). Results: We received a total of 37 runs from 14 different teams for the task. When evaluated using the gold-standard annotations of the 50 articles, the highest TAP-k scores were 0.3297 (k=5), 0.3538 (k=10), and 0.3535 (k=20), respectively. Higher TAP-k scores of 0.4916 (k=5, 10, 20) were observed when evaluated using the inferred ground truth over the full test set. When combining team results using machine learning, the best composite system achieved TAP-k scores of 0.3707 (k=5), 0.4311 (k=10), and 0.4477 (k=20) on the gold standard, representing improvements of 12.4%, 21.8%, and 26.6% over the best team results, respectively. Conclusions: By using full text and being species non-specific, the GN task in BioCreative III has moved closer to a real literature curation task than similar tasks in the past and presents additional challenges for the text mining community, as revealed in the overall team results. By evaluating teams using the gold standard, we show that the EM algorithm allows team submissions to be differentiated while keeping the manual annotation effort feasi

关键词： Confidence Score expectation maximization algorithm Name Entity Recognition Gene Mention Human Annotation

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MAP channel-estimation-based PIC receiver for downlink MC-CDMA systems

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2008年第1期000卷 570624-570624页

作者： Dogan, Hakan Panayirci, Erdal Cirpan, Hakan A. Fleury, Bernard H. Istanbul Univ Dept Elect & Elect Engn TR-34850 Istanbul Turkey Kadir Kas Univ Dept Elect Engn TR-34083 Istanbul Turkey Aalborg Hosp Dept Elect Syst Sect Navig & Commun DK-9000 Aalborg Denmark

We propose a joint MAP channel estimation and data detection technique based on the expectation maximization (EM) method with paralel interference cancelation (PIC) for downlink multicarrier (MC) code division multiple access (CDMA) systems in the presence of frequency selective channels. The quality of multiple access interference (MAI), which can be improved by using channel estimation and data estimation of all active users, affects considerably the performance of PIC detector. Therefore, data and channel estimation performance obtained in the initial stage has a significant relationship with the performance of PIC. So obviously it is necessary to make excellent joint data and channel estimation for initialization of PIC detector. The EM algorithm derived estimates the complex channel parameters of each subcarrier iteratively and generates the soft information representing the data a posterior probabilities. The soft information is then employed in a PIC module to detect the symbols efficiently. Moreover, the MAP-EM approach considers the channel variations as random processes and applies the Karhunen-Loeve (KL) orthogonal series expansion. The performance of the proposed approach is studied in terms of bit-error rate (BER) and mean square error (MSE). Throughout the simulations, extensive comparisons with previous works in literature are performed, showing that the new scheme can offer superior performance.

关键词： Mean Square Error Channel Estimation expectation maximization Code Division Multiple Access expectation maximization algorithm

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Statistical channel model for underwater wireless optical communication system under a wide range of air bubble populations

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OPTICAL ENGINEERING 2021年第3期60卷 036111-036111页

作者： Singh, Mandeep Singh, Maninder Lal Singh, Gurpreet Gill, Harpuneet Singh Guru Nanak Dev Univ Dept Elect Technol Amritsar Punjab India

The performance of the underwater wireless optical communication (UWOC) system is severely affected by the presence of air bubbles in the water. Therefore, an experimental setup is designed to investigate the effect of different sized air bubbles on the laser beam propagating through the UWOC system. A wide range of air bubbles has been generated in the water tank by varying the diameter of the holes in a copper tube and by varying the air flow rates. When an optical beam propagates through the underwater environment in the presence of air bubbles, the received optical beam undergoes severe intensity fluctuations. The distribution of these intensity fluctuations conveys very useful information to predict the behavior of the underwater channel. The distributions of these intensity fluctuations have been modeled using a Gaussian mixture model (GMM), which is the sum of Gaussian functions. The parameters of the proposed GMM model are estimated by expectation maximization algorithm to obtain maximum likelihood estimation. The goodness of fit is also performed by considering a confidence interval of (95%) to estimate the feasibility of the proposed GMM model, which provides excellent results. The proposed GMM model excellently fits the experimental data for all the considered cases. In addition, based on this proposed model and experimental observations, the performance of the UWOC system is evaluated in terms of signal-to-noise ratio, outage probability, bit error rate, maximum Q-factor, etc. The results show an exact match between the experimental and proposed theoretical results that are modeled using GMM model and hence signifies the precision of the proposed model. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： underwater wireless optical communication channel modeling Gaussian mixture model expectation maximization algorithm signal-to-noise ratio outage probability

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Learning Atom Probe Tomography time-of-flight peaks for mass-to-charge ratio spectrometry

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ULTRAMICROSCOPY 2022年 237卷 113521页

作者： Coakley, Kevin J. Sanford, Norman A. NIST 325 Broadway Boulder CO 80305 USA

In laser-assisted atom probe tomography, an important goal is to reconstruct the mass-to-charge ratio, (m/z), spectrum due to various ion species. In general, the probability mass function (pmf) associated with the time-of-flight (TOF) spectrum produced by each ion species is unknown and varies from species-to-species. Moreover, measuring pmfs for distinct ion species in calibration experiments is not practical. Here, we present a mixture model method to determine TOF pmfs that can vary from peak-to-peak. In this approach, we determine weights of candidate pmfs with a maximum likelihood method. In a proof-of-principle study, we apply our method to a TOF spectrum acquired from a silicon sample and determine intensity estimates of singly charged isotopes of silicon.

关键词： Atom Probe Tomography Cross-validation expectation maximization algorithm Machine learning Maximum likelihood Mixture model Silicon isotopes

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A new cure rate model with discrete and multiple exposures

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2024年

作者： Pal, Suvra Univ Texas Arlington Dept Math 411 S Nedderman Dr Arlington TX 76019 USA Univ Texas Arlington Coll Sci Div Data Sci Arlington TX 76019 USA

Cure rate models are mostly used to study data arising from cancer clinical trials. Its use in the context of infectious diseases has not been explored well. In 2008, Tournoud and Ecochard first proposed a mechanistic formulation of cure rate model in the context of infectious diseases with multiple exposures to infection. However, they assumed a simple Poisson distribution to capture the unobserved pathogens at each exposure time. In this paper, we propose a new cure rate model to study infectious diseases with discrete multiple exposures to infection. Our formulation captures both over-dispersion and under-dispersion with respect to the count on pathogens at each time of exposure. We also propose a new estimation method based on the expectation maximization algorithm to calculate the maximum likelihood estimates of the model parameters. We carry out a detailed Monte Carlo simulation study to demonstrate the performance of the proposed model and estimation algorithm. The flexibility of our proposed model also allows us to carry out a model discrimination. For this purpose, we use both likelihood ratio test and information-based criteria. Finally, we illustrate our proposed model using a recently collected data on COVID-19.

关键词： COVID-19 expectation maximization algorithm HIV Multiple exposures Nosocomial infection

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Mixed Gaussian Models for Modeling Fluctuation Process Characteristics of Photovoltaic Outputs

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FRONTIERS IN ENERGY RESEARCH 2019年 7卷

作者： Wang, Zhenhao Kang, Jia Cheng, Long Pei, Zheyi Dong, Cun Liang, Zhifeng Northeast Elect Power Univ Sch Elect Engn Jilin Jilin Peoples R China Natl Elect Power Dispatching & Commun Ctr Beijing Peoples R China

In order to model fluctuation process characteristics of photovoltaic (PV) outputs, this paper proposes a novel mixed Gaussian model with the expectation maximization (EM) algorithm. Firstly, random components of PV outputs are obtained through computing the difference between the measured data of PV output and its theoretical outputs. Secondly, the EM algorithm is used to determine the weight of different Gaussian distribution functions. Finally, the mixed Gaussian model is obtained by linearly superimposing these Gaussian functions with the weight. Based on the simulation results on the measured data in Xichang City, China, the effectiveness of the proposed model is verified. Furthermore, this model has proven to be significantly better than other traditional models including t location-scale (TLS) distribution model.

关键词： PV output modeling fluctuation characteristics probability density distribution expectation maximization algorithm mixed Gaussian models

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On-line harmonic signal denoising from the measurement with non-stationary and non-Gaussian noise

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SIGNAL PROCESSING 2022年 201卷

作者： Yu, Liang Chen, Yanqi Zhang, Yongli Wang, Ran Zhang, Zhaodong Shanghai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Inst Vibrat Shock & Noise Shanghai 200240 Peoples R China Hagong Intelligent Robot Co Ltd Shanghai 201100 Peoples R China Shanghai Maritime Univ Coll Logist Engn Shanghai 201306 Peoples R China

Harmonic denoising is one of the important preprocessing steps before extracting harmonic signal characteristics. Several signal processing techniques have been developed and applied for denoising the harmonics, which assume that the noise follows a Gaussian distribution and is stationary. However, the noise is often not so simple as a Gaussian distribution, and it could be non-Gaussian and non-stationary in most practical scenarios. A novel online denoising method for the harmonic signal with non-stationary complex noises based on the Bayesian Maximum a Posteriori (MAP) framework is proposed in this paper. The measured signal is divided equally into several frames. Then these frames are transformed into the time-frequency domain by the Short-Time Fourier transform (STFT) and are assumed to be the sum of a low-rank matrix and a noise matrix. The online model of the low-rank matrix and the noise matrix is then constructed between the frames. The online Gaussian mixture model (GMM) and low-rank matrix factorization are performed on the measurement matrix in the complex number domain to reconstruct the harmonic signal. The performance of the proposed method is validated in the simulations. The non-Gaussian and non-stationary noise can be removed more effectively, and the proposed algorithm can improve the frequency estimation accuracy. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Harmonic denoising Gaussian mixture model Low-rank modeling Online algorithm expectation maximization algorithm

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Iterative reconstruction techniques in emission computed tomography

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PHYSICS IN MEDICINE AND BIOLOGY 2006年第15期51卷 R541-R578页

作者： Qi, Jinyi Leahy, Richard M. Univ Calif Davis Dept Biomed Engn Davis CA 95616 USA Univ So Calif Dept Elect Engn Signal & Image Proc Inst Los Angeles CA 90089 USA

In emission tomography statistically based iterative methods can improve image quality relative to analytic image reconstruction through more accurate physical and statistical modelling of high-energy photon production and detection processes. Continued exponential improvements in computing power, coupled with the development of fast algorithms, have made routine use of iterative techniques practical, resulting in their increasing popularity in both clinical and research environments. Here we review recent progress in developing statistically based iterative techniques for emission computed tomography. We describe the different formulations of the emission image reconstruction problem and their properties. We then describe the numerical algorithms that are used for optimizing these functions and illustrate their behaviour using small scale simulations.

关键词： BAYESIAN IMAGE-RECONSTRUCTION expectation maximization algorithm MAXIMUM-LIKELIHOOD RECONSTRUCTION LIST-MODE LIKELIHOOD MEDIAN ROOT PRIOR EM algorithm LEAST-SQUARES PET DATA 3D PET SCATTER CORRECTION

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Association mapping of blood pressure levels in a longitudinal framework using binomial regression

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BMC proceedings 2014年第Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo期8卷 S74页

作者： Arunabha Majumdar Indranil Mukhopadhyay Saurabh Ghosh Human Genetics Unit Indian Statistical Institute 203 B.T. Road Kolkata 700108 India.

Heritable quantitative characters underline complex genetic traits. However, a single quantitative phenotype may not be a suitably good surrogate for a clinical end point trait. It may be more optimal to use a multivariate phenotype vector correlated with the end point trait to carry out an association analysis. Existing methods, such as variance components and principal components, suffer from inherent limitations, such as lack of robustness or difficulty in biological interpretation of association findings. In an effort to circumvent these limitations, we propose a novel regression approach based on a conditional binomial model to detect association between a single-nucleotide polymorphism and a multivariate phenotype vector. We use our proposed method to analyze data on systolic and diastolic blood pressure levels provided in Genetic Analysis Workshop 18. We find that the bivariate analysis of the two phenotypes yields more promising results in terms of lower p-values compared to univariate analyses.

关键词： Association Analysis Blood Pressure Level expectation maximization algorithm Genetic Analysis Workshop Systolic Blood Pressure Level

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Time series prediction with input noise based on the ESN and the EM and its industrial applications

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期217卷

作者： Liu, Ying Chen, Long Li, Yunchong Zhao, Jun Wang, Wei Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China Dalian Univ Technol Artificial Intelligence Inst Dalian Peoples R China

Industrial time series data usually have a high noise. In this paper, an echo state network (ESN) model with input noise is proposed to address the problem of predicting time series with noise. In the ESN, the introduction of the input noise makes it difficult to accurately estimate the non-linear states of the dynamical reservoir, therefore, in this study, the states are approximated by linearizing it through an extended Kalman filter (EKF). For the learning of the model parameters, the expectation maximization algorithm (EM) is used to iteratively update all the uncertain parameters to construct the prediction intervals, where the state estimation is performed using a forward back algorithm. To verify the effectiveness of the proposed method, two benchmark data sets and three real gas data sets from steel enterprises are used in this paper. Experimental results show that the prediction accuracy of the proposed method is better than that of the existing methods.

关键词： Industrial data Input noise Echo state network expectation maximization algorithm

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