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Search Results: 1 - 10 of 104233 matches for " Zhang Xuegong "
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Network-based group variable selection for detecting expression quantitative trait loci (eQTL)
Weichen Wang, Xuegong Zhang
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-269
Abstract: We propose a network-based group variable selection (NGVS) method for QTL detection. Our method simultaneously maps highly correlated expression traits sharing the same biological function to marker sets formed by LD. By grouping markers, complex joint activity of multiple SNPs can be considered and the dimensionality of eQTL problem is reduced dramatically. In order to demonstrate the power and flexibility of our method, we used it to analyze two simulations and a mouse obesity and diabetes dataset. We considered the gene co-expression network, grouped markers into marker sets and treated the additive and dominant effect of each locus as a group: as a consequence, we were able to replicate results previously obtained on the mouse linkage dataset. Furthermore, we observed several possible sex-dependent loci and interactions of multiple SNPs.The proposed NGVS method is appropriate for problems with high-dimensional data and high-noise background. On eQTL problem it outperforms the classical Lasso method, which does not consider biological knowledge. Introduction of proper gene expression and loci correlation information makes detecting causal markers more accurate. With reasonable model settings, NGVS can lead to novel biological findings.Genetic loci that affect the expression levels of mRNA are called expression quantitative trait loci (eQTL). Considering mRNA transcript abundance as a quantitative trait, the aim is to detect the associated genetic loci, which is the key to understanding the regulation network and disease phenotype. Thanks to the high-throughput and advanced sequencing technology, genome-wide linkage and association studies [1,2] have shown to be effective for finding causal gene loci for diseases in many species from yeast to human. The interested reader may find a detailed overview of the eQTL issues and some existing mapping methods in reviews [3,4].The simplest mapping ideas are regression-based methods, but traditional methods have some disadv
INTRODUCTION TO STATISTICAL LEARNING THEORY AND SUPPORT VECTOR MACHINES
关于统计学习理论与支持向量机

ZHANG Xuegong,
张学工

自动化学报 , 2000,
Abstract: Data-based machine learning covers a wide range of topics from pattern recognition to function regression and density estimation. Most of the existing methods are based on traditional statistics, which provides conclusion only for the situation where sample size is tending to infinity. So they may not work in practical cases of limited samples. Statistical Learning Theory or SLT is a small-sample statistics by Vapnik et al. , which concerns mainly the statistic principles when samples are limited, especially the properties of learning procedure in such cases. SLT provides us a new framework for the general learning problem, and a novel powerful learning method called Support Vector Machine or SVM, which can solve small-sample learning problems better. It is believed that the study of SLT and SVM is becoming a new hot area in the field of machine learning. This review introduces the basic ideas of SLT and SVM, their major characteristics and some current research trends.
Identifying changed protein-protein interactions in biological processes by gene coexpression analysis
Ting Zhang,XueGong Zhang,ZhiRong Sun
Chinese Science Bulletin , 2010, DOI: 10.1007/s11434-010-0114-6
Abstract: The interaction strength between 2 proteins is not constant but variable under different conditions. For a given biological process, identification of protein-protein interactions (PPIs) undergoing dynamic change in interaction strength is highly valuable but never achieved before. In this work, we presented a computational approach to identify changed PPIs (cPPIs) on a global scale by analyzing the coexpression level of genes encoding the interacting protein pairs. This approach stemmed from the biological conception that the change of protein-protein interaction bore imprint at the gene coexpression level. We applied this method to identify cPPIs in cells treated with a cytokine TGFβ, as well as cPPIs in rheumatoid arthritis (RA) patients. The accuracy of identification was evaluated by comparing our results with data from the high-throughput experiment and literature mining. Our analysis demonstrated that this is a simple and effective method to infer cPPIs from a given set of PPIs or even from the whole interactome. Further analysis uncovered the biological functions of the cPPIs in RA patients, which included muscle contraction and antigen presentation. Our method could help to elucidate molecular mechanisms of dynamic biological processes.
Observations on shifted cumulative regulation
Chao Ye, Ying Liu, Xuegong Zhang
Genome Biology , 2011, DOI: 10.1186/gb-2011-12-4-404
Abstract: Comment on He et al.: http://genomebiology.com/2007/8/9/R181 webciteStudying the collaborative effects of multiple regulators is a key to understanding the basic principles of gene regulation. He et al. [1] proposed a shifted cumulative model to dissect combinatorial gene regulation. They discovered significant correlations between the combined expression profiles of regulators and the time series of expression of their target gene. The work highlighted the importance of identifying integrative effects of multiple transcription factors and showed that this identification was possible. We did a series of experiments to study possible combinatorial regulatory mechanisms following their strategy, but we found that the correlation among three genes can increase significantly after time-shifted combination no matter whether there are regulatory relationships. Our observations led to the conclusion that such increases are not sufficient to infer cumulative regulation relations.We followed the strategy in He et al. [1] to generate combined profiles of two regulators in our experiments. Specifically, let τi(0 ≤ τi ≤ τmax
Discrimination and feature selection of geographic origins of traditional Chinese medicine herbs with NIR spectroscopy
Shuhua Liu,Xuegong Zhang,Suqin Sun
Chinese Science Bulletin , 2005, DOI: 10.1007/BF02897523
Abstract: With the traditional Chinese medicine herbsangelicae dahuricae radix (ADR or Baizhi) andsalviae miltiorrhizae radix (SMR or Danshen) as two examples, this work studies the automatic discrimination of the geographic origins of the herbs using near infrared (NIR) reflectance spectroscopy. Multi-class support vector machine (SVM) is utilized for the purpose, and recursive SVM is utilized to select the feature spectral segments that are decisive for the discrimination. With only 5 and 8 short spectral segments, discriminative accuracies of 92% are achieved on independent test sample sets. This work not only provides a prototype of accurate rapid discriminating systems for quality control of herbal medicines, but also opens new possibilities in studying subtle differences in the chemical compositions of herbs from different cultivation conditions and investigating their associations with the effectiveness of the herbs.
Complicated evolutionary patterns of microRNAs in vertebrates
XiaoWo Wang,XueGong Zhang,YanDa Li
Science China Life Sciences , 2008, DOI: 10.1007/s11427-008-0075-z
Abstract: MicroRNAs (miRNAs) are a class of ~22 nt long endogenous non-coding RNAs that play important regulatory roles in diverse organisms. Up to now, little is known about the evolutionary properties of these crucial regulators. Most miRNAs were thought to be phylogenetically conserved, but recently, a number of poorly-conserved miRNAs have been reported and miRNA innovation is shown to be an ongoing process. In this work, through the characterization of an miRNA super family, we studied the evolutionary patterns of miRNAs in vertebrates. Recently generated miRNAs seem to evolve rapidly during a certain period following their emergence. Multiple lineage-specific expansions were observed. Homolgous premiRNAs may produce mature products from the opposite stem arms following tandem duplications, which may have important contribution to miRNA innovation. Our observations of miRNAs’ complicated evolutionary patterns support the notion that these key regulatory molecules may play very active roles in evolution.
Complicated evolutionary patterns of microRNAs in vertebrates

WANG XiaoWo,ZHANG XueGong &,LI YanDa,

中国科学C辑(英文版) , 2008,
Abstract: MicroRNAs (miRNAs) are a class of approximately 22 nt long endogenous non-coding RNAs that play important regulatory roles in diverse organisms. Up to now, little is known about the evolutionary properties of these crucial regulators. Most miRNAs were thought to be phylogenetically conserved, but recently, a number of poorly-conserved miRNAs have been reported and miRNA innovation is shown to be an ongoing process. In this work, through the characterization of an miRNA super family, we studied the evolutionary patterns of miRNAs in vertebrates. Recently generated miRNAs seem to evolve rapidly during a certain period following their emergence. Multiple lineage-specific expansions were observed. Homolgous premiRNAs may produce mature products from the opposite stem arms following tandem duplications, which may have important contribution to miRNA innovation. Our observations of miRNAs' complicated evolutionary patterns support the notion that these key regulatory molecules may play very active roles in evolution.
Finding distinct biclusters from background in gene expression matrices
Zhengpeng Wu,Jiangni Ao,Xuegong Zhang
Bioinformation , 2007,
Abstract: Biclustering, or the discovery of subsets of samples and genes that are homogeneous and distinct from the background, has become an important technique in analyzing current microarray datasets. Most existing biclustering methods define a bicluster type as a fixed (predefined) pattern and then trying to get results in some searching process. In this work, we propose a novel method for finding biclusters or 2-dimensional patterns that are significantly distinct from the background without the need for pre-defining a pattern within the bicluster. The method named Distinct 2-Dimensional Pattern Finder (D2D) is composed of an iterative reordering step of the rows and columns in the matrix using a new similarity measure, and a flexible scanning-and-growing step to identify the biclusters. Experiments on a large variety of simulation data show that the method works consistently well under different conditions, whereas the existing methods compared may work well under some certain conditions but fail under some other conditions. The impact of noise levels, overlapping degrees between clusters and different setting of parameters were also investigated, which indicated that the D2D method is robust against these factors. The proposed D2D method can efficiently discover many different types of biclusters given that they have distinctive features from the background. The computer program is available upon request.
Integrating gene expression and protein-protein interaction network to prioritize cancer-associated genes
Wu Chao,Zhu Jun,Zhang Xuegong
BMC Bioinformatics , 2012, DOI: 10.1186/1471-2105-13-182
Abstract: Background To understand the roles they play in complex diseases, genes need to be investigated in the networks they are involved in. Integration of gene expression and network data is a promising approach to prioritize disease-associated genes. Some methods have been developed in this field, but the problem is still far from being solved. Results In this paper, we developed a method, Networked Gene Prioritizer (NGP), to prioritize cancer-associated genes. Applications on several breast cancer and lung cancer datasets demonstrated that NGP performs better than the existing methods. It provides stable top ranking genes between independent datasets. The top-ranked genes by NGP are enriched in the cancer-associated pathways. The top-ranked genes by NGP-PLK1, MCM2, MCM3, MCM7, MCM10 and SKP2 might coordinate to promote cell cycle related processes in cancer but not normal cells. Conclusions In this paper, we have developed a method named NGP, to prioritize cancer-associated genes. Our results demonstrated that NGP performs better than the existing methods.
Isoform reconstruction using short RNA-Seq reads by maximum likelihood is NP-hard
Tianyang Li,Rui Jiang,Xuegong Zhang
Quantitative Biology , 2013,
Abstract: Maximum likelihood is a popular technique for isoform reconstruction. Here, we show that isoform reconstruction using short RNA-Seq reads by maximum likelihood is NP-hard.
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