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Enabling multiplexed testing of pooled donor cells through whole-genome sequencing

GENOME MEDICINE 2018年第1期10卷 31-31页

作者： Chan, Yingleong Chan, Ying Kai Goodman, Daniel B. Guo, Xiaoge Chavez, Alejandro Lim, Elaine T. Church, George M. Harvard Univ Wyss Inst Biol Inspired Engn Boston MA 02115 USA Harvard Med Sch Dept Genet Boston MA 02115 USA Harvard MIT Hlth Sci & Technol Cambridge MA 02139 USA Columbia Univ Coll Phys & Surg Dept Pathol & Cell Biol 630 W 168th St New York NY 10032 USA

We describe a method that enables the multiplex screening of a pool of many different donor cell lines. Our method accurately predicts each donor proportion from the pool without requiring the use of unique DNA barcodes as markers of donor identity. Instead, we take advantage of common single nucleotide polymorphisms, whole-genome sequencing, and an algorithm to calculate the proportions from the sequencing data. By testing using simulated and real data, we showed that our method robustly predicts the individual proportions from a mixed-pool of numerous donors, thus enabling the multiplexed testing of diverse donor cells en masse. More information is available at https://***/poolseq/

关键词： Multiplexed testing Barcode free method Single nucleotide polymorphisms expectation maximization algorithm Next-generation sequencing Personal Genome Project

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Refining motifs by improving information content scores using neighborhood profile search

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algorithmS FOR MOLECULAR BIOLOGY 2006年第1期1卷 23-23页

作者： Reddy, Chandan K. Weng, Yao-Chung Chiang, Hsiao-Dong Cornell Univ Sch Elect & Comp Engn Ithaca NY 14853 USA

The main goal of the motif finding problem is to detect novel, over-represented unknown signals in a set of sequences (e.g. transcription factor binding sites in a genome). The most widely used algorithms for finding motifs obtain a generative probabilistic representation of these over-represented signals and try to discover profiles that maximize the information content score. Although these profiles form a very powerful representation of the signals, the major difficulty arises from the fact that the best motif corresponds to the global maximum of a non-convex continuous function. Popular algorithms like expectation maximization (EM) and Gibbs sampling tend to be very sensitive to the initial guesses and are known to converge to the nearest local maximum very quickly. In order to improve the quality of the results, EM is used with multiple random starts or any other powerful stochastic global methods that might yield promising initial guesses ( like projection algorithms). Global methods do not necessarily give initial guesses in the convergence region of the best local maximum but rather suggest that a promising solution is in the neighborhood region. In this paper, we introduce a novel optimization framework that searches the neighborhood regions of the initial alignment in a systematic manner to explore the multiple local optimal solutions. This effective search is achieved by transforming the original optimization problem into its corresponding dynamical system and estimating the practical stability boundary of the local maximum. Our results show that the popularly used EM algorithm often converges to suboptimal solutions which can be significantly improved by the proposed neighborhood profile search. Based on experiments using both synthetic and real datasets, our method demonstrates significant improvements in the information content scores of the probabilistic models. The proposed method also gives the flexibility in using different local solvers and global

关键词： expectation maximization expectation maximization algorithm Exit Point Motif Finding Local Optimal Solution

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Semi-automatic segmentation of myocardium at risk from contrast enhanced SSFP images - validation against manual delineation and SPECT

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Journal of Cardiovascular Magnetic Resonance 2015年第SUPPL 1期17卷 Q127-Q127页

作者： Jane Tufvesson Marcus Carlsson Anthony H Aletras Henrik Engblom Jean-Francois Deux Sasha Koul Peder Sörensson John Pernow Dan Atar David Erlinge Håkan Arheden Einar Heiberg Cardiac MR group Lund Dept. of Clinical Physiology Lund University Lund Sweden Dept of Numerical Analysis Faculty of Engineering Lund University Lund Sweden Dept of Cardiology Henri Mondor Hospital Creteil France Dept of Cardiology Lund University Lund Sweden Dept of Medicine Karolinska Institutet Karolinska University Hospital Stockholm Sweden Dept of Cardiology B Oslo University Hospital Ullevål and Faculty of Medicine University of Oslo Oslo Norway

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A multi-model identification method for the fiber stretching process based on the EM algorithm

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IFAC-PapersOnLine 2015年第8期48卷 837-842页

作者： Fan Guo Yongsheng Ding Lei Chen Lihong Ren Kuangrong Hao College of Information Science and Technology Donghua University Shanghai 201620 China Engineering Research Center of Digitized Textile & Apparel Technology Ministry of Education Shanghai 201620 China

In the fiber production process, the stretching process plays a key role in the quality of the final fiber product. Due to the fiber stretching process with inborn nonlinearity, the performance of a single controller and an optimizer may be compromised or even unsatisfactory. Thus, we consider a multi-model identification method for the fiber stretching process. The dynamic transitions among different operating points are achieved by the change of the operating conditions in the fiber stretching process. To excite all of the nonlinearity character in the fiber stretching process, the transitions among different operating conditions is achieved. The structure of each sub-models, operating points, operating range are assumed. Based on the input output data of the process, a linear parameter varying (LPV) model is built by applying a probability identification method. To achieve the smoothly connected among the different operating conditions, an exponential function is used. Then a global LPV model is constructed by synthesizing the local models. Simulated results show that the LPV method has the effectiveness in solving the inherent nonlinearity of the fiber stretching process.

关键词： LPV model Multiple model expectation maximization algorithm System identification Stretching process

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Lifetime Estimation for Multi-Phase Deteriorating Process with Random Abrupt Jumps

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SENSORS 2019年第6期19卷 1472.-1472.页

作者： Zhang, Jianxun Si, Xiaosheng Du, Dangbo Hu, Chen Hu, Changhua Xian Res Inst High Tech Dept Automat Xian 710025 Shaanxi Peoples R China Xi An Jiao Tong Univ Key Lab Educ Minist Modern Design & Rotor Bearing Syst Xian 710049 Shaanxi Peoples R China

Owing to operating condition changing, physical mutation, and sudden shocks, degradation trajectories usually exhibit multi-phase features, and the abrupt jump often appears at the changing time, which makes the traditional methods of lifetime estimation unavailable. In this paper, we mainly focus on how to estimate the lifetime of the multi-phase degradation process with abrupt jumps at the change points under the concept of the first passage time (FPT). Firstly, a multi-phase degradation model with jumps based on the Wiener process is formulated to describe the multi-phase degradation pattern. Then, we attain the lifetime's closed-form expression for the two-phase model with fixed jump relying on the distribution of the degradation state at the change point. Furthermore, we continue to investigate the lifetime estimation of the degradation process with random effect caused by unit-to-unit variability and the multi-phase degradation process. We extend the results of the two-phase case with fixed parameters to these two cases. For better implementation, a model identification method with off-line and on-line parts based on expectation maximization (EM) algorithm and Bayesian rule is proposed. Finally, a numerical case study and a practical example of gyro are provided for illustration.

关键词： life prognostics reliability multi-phase degradation expectation maximization algorithm random jump

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Collaborative filtering recommendation algorithm for e-commerce products based on Bayesian network

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International Journal of Reasoning-based Intelligent Systems 2024年第4期16卷 331-336页

作者： Wan, Hongli School of Computer Dalian Neusoft University of Information Dalian116023 China

In order to effectively improve the recommendation accuracy of collaborative screening of e-commerce products, ensure the results of collaborative screening of e-commerce products, and reduce the time consumption of collaborative screening of e-commerce products, this project plans to study the recommendation algorithm for collaborative screening of e-commerce products based on Bayesian networks. Use an identical method to determine the user selection of e-commerce platform. On this basis, a new method was adopted to conduct an overall similarity analysis of products in e-commerce platforms. Then, using Bayesian networks and expected maximum methods, establish a model of user interest in e-commerce products on e-commerce platforms, thereby completing joint screening of e-commerce products. Through testing the joint screening of different types of e-commerce products, it was found that this method had a recommendation rate of 91.6% and an accuracy rate of 95.9% in the joint screening of e-commerce products. Copyright © 2024 Inderscience Enterprises Ltd.

关键词： expectation maximization algorithm

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Dissemination of the Spirit of Public Security Models in the Age of Melting Media

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Applied Mathematics and Nonlinear Sciences 2024年第1期9卷

作者： Hou, Ming Investigation Department Henan Police College Henan Zhengzhou450046 China

Public security heroic model culture is a political-cultural phenomenon with Chinese characteristics formed by public security organs in the process of selecting, recognizing, publicizing, and appeasing heroic models. This paper researches the culture of public security heroic models and proposes a model for disseminating the spirit of public security heroic models through social role perception, called RAIN. The RAIN model unified the two complementary tasks of the information dissemination process and the social roles of users. The model maps the dissemination parameters between users and those between social roles, reducing the number of parameters. The parameters are estimated using a Gibbs sampling-based learning algorithm. At the same time, a continuous influence maximization algorithm is proposed under the independent cascade propagation model, which can calculate the incremental values in each direction by the gradient descent method and get the optimal solution of the objective function value. The communication status of the spirit of public security heroism is analyzed, and the total word frequency of all the communicated high-frequency words is 8350, which is lower than 10,000 and is at the lower-middle communication level. The total communication power value and total influence value of the spirit of public security heroes are 1042 and 4701, respectively, and the number of high communication power and high influence content is only 22 and 21, so there are obvious deficiencies in the work of communication field. This paper takes the shortcomings as a starting point and proposes a communication optimization strategy accordingly. © 2024 Ming Hou, published by Sciendo.

关键词： expectation maximization algorithm

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A Bayesian latent class extension of naive Bayesian classifier and its application to the classification of gastric cancer patients

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BMC MEDICAL RESEARCH METHODOLOGY 2023年第1期23卷 190-190页

作者： Gohari, Kimiya Kazemnejad, Anoshirvan Mohammadi, Marjan Eskandari, Farzad Saberi, Samaneh Esmaieli, Maryam Sheidaei, Ali Tarbiat Modares Univ Fac Med Sci Dept Biostat Tehran Iran Pasteur Inst Iran Biotechnol Res Ctr Dept Med Biotechnol HPGC Res Grp Tehran Iran Allameh Tabatabai Univ Dept Stat Tehran Iran Univ Tehran Med Sci Sch Publ Hlth Dept Epidemiol & Biostat Tehran Iran

BackgroundThe Naive Bayes (NB) classifier is a powerful supervised algorithm widely used in Machine Learning (ML). However, its effectiveness relies on a strict assumption of conditional independence, which is often violated in real-world scenarios. To address this limitation, various studies have explored extensions of NB that tackle the issue of non-conditional independence in the data. These approaches can be broadly categorized into two main categories: feature selection and structure *** this particular study, we propose a novel approach to enhancing NB by introducing a latent variable as the parent of the attributes. We define this latent variable using a flexible technique called Bayesian Latent Class Analysis (BLCA). As a result, our final model combines the strengths of NB and BLCA, giving rise to what we refer to as NB-BLCA. By incorporating the latent variable, we aim to capture complex dependencies among the attributes and improve the overall performance of the *** expectation-maximization (EM) algorithm and the Gibbs sampling approach were offered for parameter learning. A simulation study was conducted to evaluate the classification of the model in comparison with the ordinary NB model. In addition, real-world data related to 976 Gastric Cancer (GC) and 1189 Non-ulcer dyspepsia (NUD) patients was used to show the model's performance in an actual application. The validity of models was evaluated using the 10-fold *** presented model was superior to ordinary NB in all the simulation scenarios according to higher classification sensitivity and specificity in test data. The NB-BLCA model using Gibbs sampling accuracy was 87.77 (95% CI: 84.87-90.29). This index was estimated at 77.22 (95% CI: 73.64-80.53) and 74.71 (95% CI: 71.02-78.15) for the NB-BLCA model using the EM algorithm and ordinary NB classifier, *** considering the modification of the NB classifier, incorporating a lat

关键词： Naive Bayesian classifier Bayesian latent class analysis Gibbs sampling expectation maximization algorithm Gastric cancer

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A comparative study of different deep learning methods for time-series probabilistic residential load power forecasting

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Frontiers in Energy Research 2024年 12卷

作者： Zhou, Liangcai Zhou, Yi Liu, Linlin Zhao, Xiaoying East China Branch of State Grid Corporation Shanghai China AINERGY LLC Santa ClaraCA United States

The widespread adoption of nonlinear power electronic devices in residential settings has significantly increased the stochasticity and uncertainty of power systems. The original load power data, characterized by numerous irregular, random, and probabilistic components, adversely impacts the predictive performance of deep learning techniques, particularly neural networks. To address this challenge, this paper proposes a time-series probabilistic load power prediction technique based on the mature neural network point prediction technique, i.e., decomposing the load power data into deterministic and stochastic components. The deterministic component is predicted using deep learning neural network technology, the stochastic component is fitted with Gaussian mixture distribution model and the parameters are fitted using great expectation algorithm, after which the stochastic component prediction data is obtained using the stochastic component generation method. Using a mature neural network point prediction technique, the study evaluates six different deep learning methods to forecast residential load power. By comparing the prediction errors of these methods, the optimal model is identified, leading to a substantial improvement in prediction accuracy. Copyright © 2024 Zhou, Zhou, Liu and Zhao.

关键词： expectation maximization algorithm

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Switching Dynamic State Estimation and Event Detection for Inverter-Based Resources with Multiple Control Modes

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IEEE Transactions on Power Systems 2024年

作者： Huang, Heqing Lin, Yuzhang

The Dynamic State Estimation (DSE) for Inverter-Based Resources (IBRs) is an emerging topic as IBRs gradually replace synchronous generators (SGs) in power systems. Unlike SGs, the dynamic models of IBRs heavily depend on their control algorithms, and conventional DSE methods for SGs, which assume a unchanged state space and dynamic model, cannot handle IBRs with control mode changes in real time, particularly when the power grid operators are unaware of the current control mode of the IBRs. In response to these challenges, an expectation-maximization Sliding-Window Iterated Extended Kalman Filter (EM-SW-IEKF) method is proposed in this paper. It theoretically achieves maximum likelihood estimation under different modes through the EM algorithm, providing the most probable control mode of the system as well as the corresponding state estimate. This method is validated in various IBR systems (battery energy storage systems and solar photovoltaic systems) and under different control mode transitions (switching between grid-following and grid-forming controls and between low voltage ride through and maximum power point tracking controls). © 1969-2012 IEEE.

关键词： expectation maximization algorithm

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