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学者姓名:韩九强

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< Page ,Total 26 >
A computational model for predicting integrase catalytic domain of retrovirus. PubMed Scopus
期刊论文 | 2017 , 423 , 63-70 | Journal of theoretical biology | IF: 1.833
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Abstract :

Integrase catalytic domain (ICD) is an essential part in the retrovirus for integration reaction, which enables its newly synthesized DNA to be incorporated into the DNA of infected cells. Owing to the crucial role of ICD for the retroviral replication and the absence of an equivalent of integrase in host cells, it is comprehensible that ICD is a promising drug target for therapeutic intervention. However, annotated ICDs in UniProtKB database have still been insufficient for a good understanding of their statistical characteristics so far. Accordingly, it is of great importance to put forward a computational ICD model in this work to annotate these domains in the retroviruses. The proposed model then discovered 11,660 new putative ICDs after scanning sequences without ICD annotations. Subsequently in order to provide much confidence in ICD prediction, it was tested under different cross-validation methods, compared with other database search tools, and verified on independent datasets. Furthermore, an evolutionary analysis performed on the annotated ICDs of retroviruses revealed a tight connection between ICD and retroviral classification. All the datasets involved in this paper and the application software tool of this model can be available for free download at https://sourceforge.net/projects/icdtool/files/?source=navbar.

Keyword :

Position weight matrix Support vector machine Pseudo amino acid composition Random forest

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GB/T 7714 Wu Sijia , Han Jiuqiang , Zhang Xinman et al. A computational model for predicting integrase catalytic domain of retrovirus. [J]. | Journal of theoretical biology , 2017 , 423 : 63-70 .
MLA Wu Sijia et al. "A computational model for predicting integrase catalytic domain of retrovirus." . | Journal of theoretical biology 423 (2017) : 63-70 .
APA Wu Sijia , Han Jiuqiang , Zhang Xinman , Zhong Dexing , Liu Ruiling . A computational model for predicting integrase catalytic domain of retrovirus. . | Journal of theoretical biology , 2017 , 423 , 63-70 .
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基于多引导滤波器的单幅图像超分辨率技术 CSCD PKU
期刊论文 | 2017 , (10) , 920-927 | 红外技术
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Abstract :

提出了一种基于多引导滤波器的单幅图像超分辨率方法.首先,该方法通过大量的自然图像建立高低分辨率图像块样本训练库,并通过聚类算法将具有相似性质的高低分辨率样本块进行聚类;其次,将输入低分辨率图像进行重叠分块,并在样本库中搜索最近邻的高低分辨率样本聚类;再次,将输入低分辨率图像块作为输入图像,与样本库中最近邻的低分辨率聚类样本作为引导图像,运用本文提出的多引导滤波器计算引导滤波器的参数;最后,利用样本库中最近邻的高分辨率聚类样本和引导滤波器的参数,通过多引导滤波器就可以重构高分辨率图像.实验结果表明,本文算法不仅能很好地重构图像的高频细节,还能很好地恢复图像的纹理特征.

Keyword :

样本训练库 引导滤波器 超分辨率 高频细节

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GB/T 7714 刘哲 , 韩九强 , 黄世奇 . 基于多引导滤波器的单幅图像超分辨率技术 [J]. | 红外技术 , 2017 , (10) : 920-927 .
MLA 刘哲 et al. "基于多引导滤波器的单幅图像超分辨率技术" . | 红外技术 10 (2017) : 920-927 .
APA 刘哲 , 韩九强 , 黄世奇 . 基于多引导滤波器的单幅图像超分辨率技术 . | 红外技术 , 2017 , (10) , 920-927 .
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A computational model for predicting transmembrane regions of retroviruses SCIE PubMed Scopus
期刊论文 | 2017 , 15 (3) | JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY | IF: 0.991
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Abstract :

Transmembrane region (TR) is a conserved region of transmembrane (TM) subunit in envelope (env) glycoprotein of retrovirus. Evidences have shown that TR is responsible for anchoring the env glycoprotein on the lipid bilayer and substitution of the TR for a covalently linked lipid anchor abrogates fusion. However, universal software could not achieve sufficient accuracy as TM in env also has several motifs such as signal peptide, fusion peptide and immunosuppressive domain composed largely of hydrophobic residues. In this paper, a support vector machine-based (SVM) model is proposed to identify TRs in retroviruses. Firstly, physicochemical and evolutionary information properties were extracted as original features. And then, the feature importance was analyzed by minimum Redundancy Maximum Relevance (mRMR) feature selection criterion. Our model achieved an Sn of 0.955, Sp of 0.998, ACC of 0.995, MCC of 0.954 using 10-fold cross-validation on the training dataset. These results suggest that the proposed model can be used to predict TRs in non-annotation retroviruses and 11917, 3344, 2, 289 and 6 new putative TRs were found in HERV, HIV, HTLV, SIV, MLV, respectively.

Keyword :

10-fold cross-validation support vector machine env glycoprotein Transmembrane region

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GB/T 7714 Liu, Ze , Lv, Hongqiang , Han, Jiuqiang et al. A computational model for predicting transmembrane regions of retroviruses [J]. | JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY , 2017 , 15 (3) .
MLA Liu, Ze et al. "A computational model for predicting transmembrane regions of retroviruses" . | JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY 15 . 3 (2017) .
APA Liu, Ze , Lv, Hongqiang , Han, Jiuqiang , Liu, Ruiling . A computational model for predicting transmembrane regions of retroviruses . | JOURNAL OF BIOINFORMATICS AND COMPUTATIONAL BIOLOGY , 2017 , 15 (3) .
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A computational method for prediction of matrix proteins in endogenous retroviruses SCIE PubMed Scopus
期刊论文 | 2017 , 12 (5) | PLOS ONE | IF: 2.766
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Abstract :

Human endogenous retroviruses (HERVs) encode active retroviral proteins, which may be involved in the progression of cancer and other diseases. Matrix protein (MA), in groupspecific antigen genes (gag) of retroviruses, is associated with the virus envelope glycoproteins in most mammalian retroviruses and may be involved in virus particle assembly, transport and budding. However, the amount of annotated MAs in ERVs is still at a low level so far. No computational method to predict the exact start and end coordinates of MAs in gags has been proposed yet. In this paper, a computational method to identify MAs in ERVs is proposed. A divide and conquer technique was designed and applied to the conventional prediction model to acquire better results when dealing with gene sequences with various lengths. Initiation sites and termination sites were predicted separately and then combined according to their intervals. Three different algorithms were applied and compared: weighted support vector machine (WSVM), weighted extreme learning machine (WELM) and random forest (RF). G-mean (geometric mean of sensitivity and specificity) values of initiation sites and termination sites under 5-fold cross validation generated by random forest models are 0.9869 and 0.9755 respectively, highest among the algorithms applied. Our prediction models combine RF & WSVM algorithms to achieve the best prediction results. 98.4% of all the collected ERV sequences with complete MAs (125 in total) could be predicted exactly correct by the models. 94,671 HERV sequences from 118 families were scanned by the model, 104 new putative MAs were predicted in human chromosomes. Distributions of the putative MAs and optimizations of model parameters were also analyzed. The usage of our predicting method was also expanded to other retroviruses and satisfying results were acquired.

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GB/T 7714 Ma, Yucheng , Liu, Ruiling , Lv, Hongqiang et al. A computational method for prediction of matrix proteins in endogenous retroviruses [J]. | PLOS ONE , 2017 , 12 (5) .
MLA Ma, Yucheng et al. "A computational method for prediction of matrix proteins in endogenous retroviruses" . | PLOS ONE 12 . 5 (2017) .
APA Ma, Yucheng , Liu, Ruiling , Lv, Hongqiang , Han, Jiuqiang , Zhong, Dexing , Zhang, Xinman . A computational method for prediction of matrix proteins in endogenous retroviruses . | PLOS ONE , 2017 , 12 (5) .
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A computational model for predicting integrase catalytic domain of retrovirus SCIE
期刊论文 | 2017 , 423 , 63-70 | JOURNAL OF THEORETICAL BIOLOGY | IF: 1.833
Abstract&Keyword Cite

Abstract :

Integrase catalytic domain (ICD) is an essential part in the retrovirus for integration reaction, which enables its newly synthesized DNA to be incorporated into the DNA of infected cells. Owing to the crucial role of ICD for the retroviral replication and the absence of an equivalent of integrase in host cells, it is comprehensible that ICD is a promising drug target for therapeutic intervention. However, annotated ICDs in UniProtKB database have still been insufficient for a good understanding of their statistical characteristics so far. Accordingly, it is of great importance to put forward a computational ICD model in this work to annotate these domains in the retroviruses. The proposed model then discovered 11,660 new putative ICDs after scanning sequences without ICD annotations. Subsequently in order to provide much confidence in ICD prediction, it was tested under different cross-validation methods, compared with other database search tools, and verified on independent datasets. Furthermore, an evolutionary analysis performed on the annotated ICDs of retroviruses revealed a tight connection between ICD and retroviral classification. All the datasets involved in this paper and the application software tool of this model can be available for free download at http://sourceforge.net/project/icdtool/files/?source-navbar. (C) 2017 Elsevier Ltd. All rights reserved.

Keyword :

Position weight matrix Support vector machine Pseudo amino acid composition Random forest

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GB/T 7714 Wu, Sijia , Han, Jiuqiang , Zhang, Xinman et al. A computational model for predicting integrase catalytic domain of retrovirus [J]. | JOURNAL OF THEORETICAL BIOLOGY , 2017 , 423 : 63-70 .
MLA Wu, Sijia et al. "A computational model for predicting integrase catalytic domain of retrovirus" . | JOURNAL OF THEORETICAL BIOLOGY 423 (2017) : 63-70 .
APA Wu, Sijia , Han, Jiuqiang , Zhang, Xinman , Zhong, Dexing , Liu, Ruiling . A computational model for predicting integrase catalytic domain of retrovirus . | JOURNAL OF THEORETICAL BIOLOGY , 2017 , 423 , 63-70 .
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基于多引导滤波器的单幅图像超分辨率技术 CSCD PKU
期刊论文 | 2017 , (10) | 红外技术
Abstract&Keyword Cite

Keyword :

样本训练库 引导滤波器 超分辨率 高频细节

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GB/T 7714 刘哲 , 韩九强 , 黄世奇 . 基于多引导滤波器的单幅图像超分辨率技术 [J]. | 红外技术 , 2017 , (10) .
MLA 刘哲 et al. "基于多引导滤波器的单幅图像超分辨率技术" . | 红外技术 10 (2017) .
APA 刘哲 , 韩九强 , 黄世奇 . 基于多引导滤波器的单幅图像超分辨率技术 . | 红外技术 , 2017 , (10) .
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A Computational Method for Identification of Disease-associated Non-coding SNPs in Human Genome EI CPCI-S Scopus
会议论文 | 2017 , 125-129 | 16th IEEE/ACIS International Conference on Computer and Information Science (ICIS)
SCOPUS Cited Count: 1
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Abstract :

Accurate identification of functionally relevant variants against the ubiquitous background genetic variations is a significant challenge facing bioinformatics researchers and the challenge becomes more severe for non-coding variants. In this study, a novel computational method to identify candidate disease-associated non-coding single nucleotide polymorphisms (SNPs) of human genome is presented. To characterize SNPs, an extensive range of features, such as sequence context, DNA structure, evolutionary conservation and histone modification signals etc. are extracted. Then random forest is adopted to build the classifier model together with an ensemble method to deal with unbalanced data. 10-fold cross-validation result shows that the proposed method can achieve accuracy with the area under ROC curve (AUC) of 0.74. All the original data and the source matlab codes involved are available at https://sourceforge.net/projects/dissnp-predict/.

Keyword :

SNPs Imbalanced data Random forest Histone modification signals

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GB/T 7714 Han, Jiuqiang , Li, Rong , Zhang, Xinman et al. A Computational Method for Identification of Disease-associated Non-coding SNPs in Human Genome [C] . 2017 : 125-129 .
MLA Han, Jiuqiang et al. "A Computational Method for Identification of Disease-associated Non-coding SNPs in Human Genome" . (2017) : 125-129 .
APA Han, Jiuqiang , Li, Rong , Zhang, Xinman , Lv, Hongqiang , Zhong, Dexing . A Computational Method for Identification of Disease-associated Non-coding SNPs in Human Genome . (2017) : 125-129 .
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A novel method for in silico identification of regulatory SNPs in human genome SCIE PubMed Scopus
期刊论文 | 2017 , 415 , 84-89 | JOURNAL OF THEORETICAL BIOLOGY | IF: 1.833
WoS CC Cited Count: 2 SCOPUS Cited Count: 2
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Abstract :

Regulatory single nucleotide polymorphisms (rSNPs), kind of functional noncoding genetic variants, can affect gene expression in a regulatory way, and they are thought to be associated with increased susceptibilities to complex diseases. Here a novel computational approach to identify potential rSNPs is presented. Different from most other rSNPs finding methods which based on hypothesis that SNPs causing large allele-specific changes in transcription factor binding affinities are more likely to play regulatory functions, we use a set of documented experimentally verified rSNPs and nonfunctional background SNPs to train classifiers, so the discriminating features are found. To characterize variants, an extensive range of characteristics, such as sequence context, DNA structure and evolutionary conservation etc. are analyzed. Support vector machine is adopted to build the classifier model together with an ensemble method to deal with unbalanced data. 10-fold cross-validation result shows that our method can achieve accuracy with sensitivity of similar to 78% and specificity of similar to 82%. Furthermore, our method performances better than some other algorithms based on aforementioned hypothesis in handling false positives. The original data and the source matlab codes involved are available at https://sourceforge.net/ projects/rsnppredict/.

Keyword :

Hydroxyl radical cleavage patterns Imbalanced data Support vector machine Position weight matrix

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GB/T 7714 Li, Rong , Zhong, Dexing , Liu, Ruiling et al. A novel method for in silico identification of regulatory SNPs in human genome [J]. | JOURNAL OF THEORETICAL BIOLOGY , 2017 , 415 : 84-89 .
MLA Li, Rong et al. "A novel method for in silico identification of regulatory SNPs in human genome" . | JOURNAL OF THEORETICAL BIOLOGY 415 (2017) : 84-89 .
APA Li, Rong , Zhong, Dexing , Liu, Ruiling , Lv, Hongqiang , Zhang, Xinman , Liu, Jun et al. A novel method for in silico identification of regulatory SNPs in human genome . | JOURNAL OF THEORETICAL BIOLOGY , 2017 , 415 , 84-89 .
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A filter feature selection method based on the Maximal Information Coefficient and Gram-Schmidt Orthogonalization for biomedical data mining EI SCIE PubMed Scopus
期刊论文 | 2017 , 89 , 264-274 | COMPUTERS IN BIOLOGY AND MEDICINE | IF: 2.115
WoS CC Cited Count: 7 SCOPUS Cited Count: 12
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Abstract :

A filter feature selection technique has been widely used to mine biomedical data. Recently, in the classical filter method minimal-Redundancy-Maximal-Relevance (mRMR), a risk has been revealed that a specific part of the redundancy, called irrelevant redundancy, may be involved in the minimal-redundancy component of this method. Thus, a few attempts to eliminate the irrelevant redundancy by attaching additional procedures to mRMR, such as Kernel Canonical Correlation Analysis based mRMR (KCCAmRMR), have been made. In the present study, a novel filter feature selection method based on the Maximal Information Coefficient (MIC) and Gram-Schmidt Orthogonalization (GSO), named Orthogonal MIC Feature Selection (OMICFS), was proposed to solve this problem. Different from other improved approaches under the max-relevance and min-redundancy criterion, in the proposed method, the MIC is used to quantify the degree of relevance between feature variables and target variable, the GSO is devoted to calculating the orthogonalized variable of a candidate feature with respect to previously selected features, and the max-relevance and min-redundancy can be indirectly optimized by maximizing the MIC relevance between the GSO orthogonalized variable and target. This orthogonalization strategy allows OMICFS to exclude the irrelevant redundancy without any additional procedures. To verify the performance, OMICFS was compared with other filter feature selection methods in terms of both classification accuracy and computational efficiency by conducting classification experiments on two types of biomedical datasets. The results showed that OMICFS outperforms the other methods in most cases. In addition, differences between these methods were analyzed, and the application of OMICFS in the mining of high-dimensional biomedical data was discussed. The Matlab code for the proposed method is available at https://github.com/ Ihqxinghun/bioinformatics/tree/master/OMICFS/.

Keyword :

Filter feature selection Gram-Schmidt Orthogonalization (GSO) Maximal Information Coefficient (MIC) Biomedical data mining

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GB/T 7714 Lyu, Hongqiang , Wan, Mingxi , Han, Jiuqiang et al. A filter feature selection method based on the Maximal Information Coefficient and Gram-Schmidt Orthogonalization for biomedical data mining [J]. | COMPUTERS IN BIOLOGY AND MEDICINE , 2017 , 89 : 264-274 .
MLA Lyu, Hongqiang et al. "A filter feature selection method based on the Maximal Information Coefficient and Gram-Schmidt Orthogonalization for biomedical data mining" . | COMPUTERS IN BIOLOGY AND MEDICINE 89 (2017) : 264-274 .
APA Lyu, Hongqiang , Wan, Mingxi , Han, Jiuqiang , Liu, Ruiling , Wang, Cheng . A filter feature selection method based on the Maximal Information Coefficient and Gram-Schmidt Orthogonalization for biomedical data mining . | COMPUTERS IN BIOLOGY AND MEDICINE , 2017 , 89 , 264-274 .
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MultiModal-Database-XJTU: An Available Database for Biometrics Recognition with Its Performance Testing EI CPCI-S Scopus
会议论文 | 2017 , 521-526 | 3rd IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)
WoS CC Cited Count: 1 SCOPUS Cited Count: 2
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Abstract :

The current need for large multimodal databases to evaluate automatic biometrics recognition systems has motivated the development of the XJTU multimodal database. The main purpose has been to consider a large scale population, with statistical significance, in a real multimodal procedure, and including several sources of variability that can be found in real environments. The acquisition process, contents and availability of the single-session baseline corpus are fully described. Some experiments showing consistency of data through the different acquisition sites and assessing data quality are also presented. MultiModal-Database-XJTU, a new multimodal database, is presented. The database consists of fingerprint images acquired with sensor, frontal face images from a camera, iris images from a Cannon scanner, and voice utterances acquired with a microphone. The MultiModal-Database-XJTU includes real multimodal data from 102 individuals. In this contribution, the acquisition setup and protocol are outlined, and the contents of the database are described. The database will be publicly available for research purposes.

Keyword :

Quantum particle swarm optimization Superior speed Multi-focus image fusion Perfect reconstruction

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GB/T 7714 Shang, Dongpeng , Zhang, Xinman , Han, Jiuqiang et al. MultiModal-Database-XJTU: An Available Database for Biometrics Recognition with Its Performance Testing [C] . 2017 : 521-526 .
MLA Shang, Dongpeng et al. "MultiModal-Database-XJTU: An Available Database for Biometrics Recognition with Its Performance Testing" . (2017) : 521-526 .
APA Shang, Dongpeng , Zhang, Xinman , Han, Jiuqiang , Xu, Xuebin . MultiModal-Database-XJTU: An Available Database for Biometrics Recognition with Its Performance Testing . (2017) : 521-526 .
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