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Search Results: 1 - 10 of 34903 matches for " Xiaobo Zhou "
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Cancer bioinformatics: detection of chromatin states, SNP-containing motifs, and functional enrichment modules
Xiaobo Zhou
Chinese Journal of Cancer , 2013, DOI: 10.5732/cjc.013.10045
Abstract: In this editorial preface, I briefly review cancer bioinformatics and introduce the four articles in this special issue highlighting important applications of the field: detection of chromatin states; detection of SNP-containing motifs and association with transcription factor-binding sites; improvements in functional enrichment modules; and gene association studies on aging and cancer. We expect this issue to provide bioinformatics scientists, cancer biologists, and clinical doctors with a better understanding of how cancer bioinformatics can be used to identify candidate biomarkers and targets and to conduct functional analysis.
Blind Deconvolution of Seismic Data Based on the Spearman’s Rho  [PDF]
Rongrong Wang, Fei Xu, Xiaobo Zhou
Journal of Computer and Communications (JCC) , 2015, DOI: 10.4236/jcc.2015.33004
Abstract:

In this paper, we propose a novel seismic blind deconvolution approach based on the Spearman’s rho in the case of band-limited seismic data with a low dominant frequency and short data records. The Spearman’s rho is a measure of the dependence between two continuous random variables without the influence of the marginal distributions, by which a new criterion for blind deconvolution is constructed. The optimization program for new criterion of blind deconvolution is performed by applying Neidell’s wavelet model to the inverse filter. The noise-free and noisy synthetic data, onshore seismic trace in the Ordos Basin, and offshore stacked section in the Bohai Bay Basin examples show good results of the method.

Detection and characterization of regulatory elements using probabilistic conditional random field and hidden Markov models
Hongyan Wang,Xiaobo Zhou
Chinese Journal of Cancer , 2013, DOI: 10.5732/cjc.012.10112
Abstract: By altering the electrostatic charge of histones or providing binding sites to protein recognition mole-cules, Chromatin marks have been proposed to regulate gene expression, a property that has motivated researchers to link these marks to cis-regulatory elements. With the help of next generation sequencing technologies, we can now correlate one specific chromatin mark with regulatory elements (e.g. enhancers or promoters) and also build tools, such as hidden Markov models, to gain insight into mark combinations. However, hidden Markov models have limitation for their character of generative models and assume that a current observation depends only on a current hidden state in the chain. Here, we employed two graphical probabilistic models, namely the linear conditional random field model and multivariate hidden Markov model, to mark gene regions with different states based on recurrent and spatially coherent character of these eight marks. Both models revealed chromatin states that may correspond to enhancers and promoters, transcribed regions, transcriptional elongation, and low-signal regions. We also found that the linear conditional random field model was more effective than the hidden Markov model in recognizing regulatory elements, such as promoter-, enhancer-, and transcriptional elongation-associated regions, which gives us a better choice.
Video-based vehicle tracking considering occlusion
Zhu Zhou, , Lu Xiaobo
- , 2015, DOI: 10.3969/j.issn.1003-7985.2015.02.019
Abstract: To track the vehicles under occlusion, a vehicle tracking algorithm based on blocks is proposed. The target vehicle is divided into several blocks of uniform size, in which the edge block can overlap its neighboring blocks. All the blocks’ motion vectors are estimated, and the noise motion vectors are detected and adjusted to decrease the error of motion vector estimation. Then, by moving the blocks based on the adjusted motion vectors, the vehicle is tracked. Aiming at the occlusion between vehicles, a Markov random field is established to describe the relationship between the blocks in the blocked regions. The neighborhood of blocks is defined using the Euclidean distance. An energy function is defined based on the blocks’ histograms and optimized by the simulated annealing algorithm to segment the occlusion region. Experimental results demonstrate that the proposed algorithm can track vehicles under occlusion accurately.
The Return-Risk Performance——The Comparison of Asset Portfolio Performance of Institution Fund with That Based on Multifractal Detrended Fluctuation Approach  [PDF]
Xiaobo Wen, Hui Wang, Zongfang Zhou, Hua Zhang
Modern Economy (ME) , 2012, DOI: 10.4236/me.2012.34059
Abstract: In this paper, we compare the portfolio allocation model of multifractal detrended Fluctuation approach with the modern efficient frontier model and the asset allocation model from Chinese institution fund, the risk-return performance of the multifractal detrended Fluctuation turns out to be more optimal portfolio allocation than that from chinese institution fund and the conclusions have implications for modern financial theory, it suggest that there is scope for more general multifractal portfolio selection models to be developed.
Invariance in the Seasonal Median Dates for Mono-Modal Monsoonal Rainfall Distribution over the Semi-Arid Ecotone of Sub-Saharan West Africa  [PDF]
Naraine Persaud, Moustafa Elrashidi, Xiaobo Zhou, Xining Zhao, Xiaoli Chen
International Journal of Geosciences (IJG) , 2013, DOI: 10.4236/ijg.2013.46A2001
Abstract:

Seasonal distribution of mono-modal, monsoonal rainfall across the semi-arid ecotone of sub-Saharan of West Africa is highly variable and unpredictable. The ever-present risk of drought and crop failure in this environment often results in food shortages that are met by emergency food aid. Humanitarian assistance planners would be better prepared for such interventions in a timely manner if they have reliable indicators that forewarn the impending failure of the rains. A good indicator would be a characteristic of the seasonal rainfall distribution that can be shown to be reasonably invariant over time and space. The objective of this study is to investigate whether such invariance existed for the seasonal median date (meaning the date when 50% of the seasonal total occurs). Such invariance is expected since the sun’s cyclic declination forces the advance and retreat of the Inter-tropical Front over West Africa. We examined the statistical properties of the seasonal median date for 1349 station-years of rainfall records for 30 rainfall stations in Burkina Faso and Niger with coordinates ranging from 9.88° to 18.5° north latitude and -4.77° to 13.2° longitude. The results showed that the median date was quite narrowly distributed over years with rather weak dependence on geographical coordinates. It can therefore be used as a reasonable ex-ante indicator of the success or failure of the rains as the rainy season progress.

Protein structure similarity from principle component correlation analysis
Xiaobo Zhou, James Chou, Stephen TC Wong
BMC Bioinformatics , 2006, DOI: 10.1186/1471-2105-7-40
Abstract: We measure structural similarity between proteins by correlating the principle components of their secondary structure interaction matrix. In our approach, the Principle Component Correlation (PCC) analysis, a symmetric interaction matrix for a protein structure is constructed with relationship parameters between secondary elements that can take the form of distance, orientation, or other relevant structural invariants. When using a distance-based construction in the presence or absence of encoded N to C terminal sense, there are strong correlations between the principle components of interaction matrices of structurally or topologically similar proteins.The PCC method is extensively tested for protein structures that belong to the same topological class but are significantly different by RMSD measure. The PCC analysis can also differentiate proteins having similar shapes but different topological arrangements. Additionally, we demonstrate that when using two independently defined interaction matrices, comparison of their maximum eigenvalues can be highly effective in clustering structurally or topologically similar proteins. We believe that the PCC analysis of interaction matrix is highly flexible in adopting various structural parameters for protein structure comparison.Conformational resemblance between proteins, whether remote or close, is often used to infer functional properties of proteins and to reveal distant evolutionary relationships between two proteins exhibiting no similarity in their amino acid sequences. Traditionally, high-resolution structure determination succeeds the biological and biochemical studies of proteins to further provide mechanistic details of the function of proteins. The biological function of these proteins have usually been suggested prior to their structural studies by in vitro binding assays, in vivo gene knock-out experiments, and sequence homology with proteins of known function. However, with the completion of the sequencing o
Nonlinear fusion filters based on prediction and smoothing
Qiansheng Cheng,Xiaobo Zhou,Xichen Sun
Chinese Science Bulletin , 2000, DOI: 10.1007/BF02898996
Abstract: To attenuate white noise, nonstationary noise and impulse noise are important for signal processing. In this letter, we present nonlinear fusion filters (NFF) based on prediction and smoothing. By means of least square fitting of a polynomial, we define and give the operators of left prediction and right prediction, left smoothing and right smoothing, central smoothing and cross-validation smoothing. In simulated experiments, it is shown that the present method is an effective one.
Hypergraph based Model and Architecture for Planet Surface Networks and Orbit Access
Xiaobo Wang,Xianwei Zhou,Junde Song
Journal of Networks , 2012, DOI: 10.4304/jnw.7.4.723-729
Abstract: In the future, it is important to construct infrastructure on the surface of deep space planets, and then networking can be achieved to support both the communication between surface network nodes and planet satellite access. And because multiple access is an important technique in deep space communication, a scene of deep space exploration was proposed based on multiple access, which include planet surface network and satellite access network. Then hypergraph theory was used to model the network, thus provide a new way to improve the network connectivity, save frequency spectrum resource and reduce mutual interference, and also how to construct a hyperedge was described. According to the network model, a novel 7-layer network architecture was introduced.
Gene Prediction Using Multinomial Probit Regression with Bayesian Gene Selection
Edward R. Dougherty,Xiaodong Wang,Xiaobo Zhou
EURASIP Journal on Advances in Signal Processing , 2004, DOI: 10.1155/s1687617204309157
Abstract: A critical issue for the construction of genetic regulatory networks is the identification of network topology from data. In the context of deterministic and probabilistic Boolean networks, as well as their extension to multilevel quantization, this issue is related to the more general problem of expression prediction in which we want to find small subsets of genes to be used as predictors of target genes. Given some maximum number of predictors to be used, a full search of all possible predictor sets is combinatorially prohibitive except for small predictors sets, and even then, may require supercomputing. Hence, suboptimal approaches to finding predictor sets and network topologies are desirable. This paper considers Bayesian variable selection for prediction using a multinomial probit regression model with data augmentation to turn the multinomial problem into a sequence of smoothing problems. There are multiple regression equations and we want to select the same strongest genes for all regression equations to constitute a target predictor set or, in the context of a genetic network, the dependency set for the target. The probit regressor is approximated as a linear combination of the genes and a Gibbs sampler is employed to find the strongest genes. Numerical techniques to speed up the computation are discussed. After finding the strongest genes, we predict the target gene based on the strongest genes, with the coefficient of determination being used to measure predictor accuracy. Using malignant melanoma microarray data, we compare two predictor models, the estimated probit regressors themselves and the optimal full-logic predictor based on the selected strongest genes, and we compare these to optimal prediction without feature selection.
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