A text mining algorithm named HMM-TFM (Hidden Markov Model based transcription factor name mining) is presented. The proposed algorithm does not need a dictionary of transcription factor names. A small verb set is def...
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In this paper we use an improved Particle Swarm Optimization algorithm to solve Multiple Sequence Alignment (MSA). MSA is a key problem in bioinformatics. The thesis starts with the theory of Particle swarm optimizati...
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engineering systems have become quite complicated in recent days. The requirements of design are complex and it is hard to meet them by considering only one discipline. In this paper, we suggest a hybrid Multi-objecti...
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In this paper, a hybrid TS-DE algorithm based on Tabu search and differential evolution algorithm is proposed to solve the reliability redundancy optimization problem. A differential evolution algorithm is embedded in...
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In this paper, a hybrid TS-DE algorithm based on Tabu search and differential evolution algorithm is proposed to solve the reliability redundancy optimization problem. A differential evolution algorithm is embedded in Tabu search algorithm. TS is applied for searching solutions space, and DE is used for generating neighborhood solutions. The advantages of both algorithms are considered simultaneously. And an adaptive hybrid TS-DE approach is developed to solve three benchmark reliability redundancy allocation problems. By comparing with other algorithms reported in previous literatures, experimental results show that the proposed method is effective and efficient for solving the reliability redundancy optimization problem.
During an epidemic, the spatial, temporal and demographical patterns of disease transmission are determined by multiple factors. Besides the physiological properties of pathogenes and hosts, the social contacts of hos...
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ISBN:
(纸本)9781479943012
During an epidemic, the spatial, temporal and demographical patterns of disease transmission are determined by multiple factors. Besides the physiological properties of pathogenes and hosts, the social contacts of host population, which characterize individuals' reciprocal exposures of infection in view of demographical structures and various social activities, are also pivotal to understand and further predict the prevalence of infectious diseases. The means of measuring social contacts will dominate the extent how precisely we can forecast the dynamics of infections in the real world. Most current works focus their efforts on modeling the spatial patterns of static social contacts. In this work, we address the problem on how to characterize and measure dynamical social contacts during an epidemic from a novel perspective. We propose an epidemic-model-based tensor deconvolution framework to address this issue, in which the spatiotemporal patterns of social contacts are represented by the factors of tensors, which can be discovered by a tensor deconvolution procedure with an integration of epidemic models from rich types of data, mainly including heterogeneous outbreak surveillance, social-demographic census and physiological data from medical reports. Taking SIR model as a case study, the efficacy of the proposed method is theoretically analyzed and empirically validated through a set of rigorous experiments on both synthetic and real-world data.
In this paper, on the basis of principal component analysis, we use least squares support vector machine (LS-SVM) to predict tRNA. Appearance frequencies of single nucleotide, 2-nucleotides, (G-C)% and (A-T)% were cho...
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The past decade has witnessed the rapid development of search engines, which has become an indispensable part of everyday life. However, people are no longer satisfied with accessing to ordinary information, and they ...
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The replication of the non-structure data from one data center to another is an urgent task in HBase. The paper studies the priority growth probability of the priority replication queue and proposed a dynamic priority...
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A huge amount of microarray datasets are produced with big number of genes and small samples. Feature selection methods have become a very sharp tool to select the gene signatures from the whole gene set. In recent ye...
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A huge amount of microarray datasets are produced with big number of genes and small samples. Feature selection methods have become a very sharp tool to select the gene signatures from the whole gene set. In recent years, researchers are concerned much about the datasets containing samples of cancer as well as corresponding control tissues. However, few feature selection methods consider the effect of paired samples. In this article, we propose a new feature selection method for paired microarray datasets based on the original paired t-test approach. We apply on the paired datasets across six common cancer types. Through comparison with some widely used methods on the performance of prediction power, stability of gene lists and functional stability, our method shows excellent performance. The proposed method has good effectiveness, stability and consistency, which enables the method to be applicative to feature selection for paired microarray expression data analysis.
A mathematical model using the spline functional as smooth constraints was presented. The second-and fourth-order partial differential equations constraints were two special cases in the model. The necessary condition...
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A mathematical model using the spline functional as smooth constraints was presented. The second-and fourth-order partial differential equations constraints were two special cases in the model. The necessary condition for optical flow minimization problem solution was also presented. This model provided a basis for formal representation and numerical computation of optical flow from a methodological point of view. The significance of this mathematical model lay in the simplification of the equations for optical flow computation into linear algebraic equations. The simplification can contribute to discrete representation of the optical flow equation, and also verify that the use of smoothness constraints can ensure the existence and uniqueness of the solution from the view of the algebraic equations.
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