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学者姓名:韩九强

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< Page ,Total 23 >
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
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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 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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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)
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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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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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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: 1 SCOPUS Cited Count: 3
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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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A computational method to predict carbonylation sites in yeast proteins SCIE PubMed Scopus
期刊论文 | 2016 , 15 (2) | GENETICS AND MOLECULAR RESEARCH
WoS CC Cited Count: 2
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Abstract :

Several post-translational modifications (PTM) have been discussed in literature. Among a variety of oxidative stress-induced PTM, protein carbonylation is considered a biomarker of oxidative stress. Only certain proteins can be carbonylated because only four amino acid residues, namely lysine (K), arginine (R), threonine (T) and proline (P), are susceptible to carbonylation. The yeast proteome is an excellent model to explore oxidative stress, especially protein carbonylation. Current experimental approaches in identifying carbonylation sites are expensive, time-consuming and limited in their abilities to process proteins. Furthermore, there is no bioinformational method to predict carbonylation sites in yeast proteins. Therefore, we propose a computational method to predict yeast carbonylation sites. This method has total accuracies of 86.32, 85.89, 84.80, and 86.80% in predicting the carbonylation sites of K, R, T, and P, respectively. These results were confirmed by 10-fold cross-validation. The ability to identify carbonylation sites in different kinds of features was analyzed and the position-specific composition of the modification site-flanking residues was discussed. Additionally, a software tool has been developed to help with the calculations in this method. Datasets and the software are available at https://sourceforge.net/projects/hqlstudio/files/CarSpred.Y/.

Keyword :

Carbonylation site prediction Yeast carbonylation CarSPred.Y

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GB/T 7714 Lv, H. Q. , Liu, J. , Han, J. Q. et al. A computational method to predict carbonylation sites in yeast proteins [J]. | GENETICS AND MOLECULAR RESEARCH , 2016 , 15 (2) .
MLA Lv, H. Q. et al. "A computational method to predict carbonylation sites in yeast proteins" . | GENETICS AND MOLECULAR RESEARCH 15 . 2 (2016) .
APA Lv, H. Q. , Liu, J. , Han, J. Q. , Zheng, J. G. , Liu, R. L. . A computational method to predict carbonylation sites in yeast proteins . | GENETICS AND MOLECULAR RESEARCH , 2016 , 15 (2) .
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A computational model for predicting fusion peptide of retroviruses EI SCIE PubMed Scopus
期刊论文 | 2016 , 61 , 245-250 | COMPUTATIONAL BIOLOGY AND CHEMISTRY | IF: 1.331
WoS CC Cited Count: 2
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Abstract :

As a pivotal domain within envelope protein, fusion peptide (FP) plays a crucial role in pathogenicity and therapeutic intervention. Taken into account the limited FP annotations in NCBI database and absence of FP prediction software, it is urgent and desirable to develop a bioinformatics tool to predict new putative FPs (np-FPs) in retroviruses. In this work, a sequence-based FP model was proposed by combining Hidden Markov Method with similarity comparison. The classification accuracies are 91.97% and 92.31% corresponding to 10-fold and leave-one-out cross-validation. After scanning sequences without FP annotations, this model discovered 53,946 np-FPs. The statistical results on FPs or np-FPs reveal that FP is a conserved and hydrophobic domain. The FP software programmed for windows environment is available at https://sourceforge.net/projects/fptool/files/?source=navbar. (C) 2016 Elsevier Ltd. All rights reserved.

Keyword :

Fusion peptide domain prediction Similarity comparison Hidden Markov Method

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GB/T 7714 Wu, Sijia , Han, Jiuqiang , Liu, Ruiling et al. A computational model for predicting fusion peptide of retroviruses [J]. | COMPUTATIONAL BIOLOGY AND CHEMISTRY , 2016 , 61 : 245-250 .
MLA Wu, Sijia et al. "A computational model for predicting fusion peptide of retroviruses" . | COMPUTATIONAL BIOLOGY AND CHEMISTRY 61 (2016) : 245-250 .
APA Wu, Sijia , Han, Jiuqiang , Liu, Ruiling , Liu, Jun , Lv, Hongqiang . A computational model for predicting fusion peptide of retroviruses . | COMPUTATIONAL BIOLOGY AND CHEMISTRY , 2016 , 61 , 245-250 .
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Computational identification of circular RNAs based on conformational and thermodynamic properties in the flanking introns EI SCIE PubMed Scopus
期刊论文 | 2016 , 61 , 221-225 | COMPUTATIONAL BIOLOGY AND CHEMISTRY | IF: 1.331
WoS CC Cited Count: 3
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Abstract :

Circular RNAs (circRNAs) were found more than 30 years ago, but have been treated as molecular flukes in a long time. Combining deep sequencing studies with bioinformatics technique, thousands of endogenous circRNAs have been found in mammalian cells, and some researchers have proved that several circRNAs act as competing endogenous RNAs (ceRNAs) to regulate gene expression. However, the mechanism by which the precursor mRNA to be transformed into a circular RNA or a linear mRNA is largely unknown. In this paper, we attempted to bioinformatically identify shared genomic features that might further elucidate the mechanism of formation and proposed a SVM-based model to distinguish circRNAs from non-circularized, expressed exons. Firstly, conformational and thermodynamic dinucleotide properties in the flanking introns were extracted as potential features. Secondly, two feature selection methods were applied to gain the optimal feature subset. Our 10-fold cross-validation results showed that the model can be used to distinguish circRNAs from non-circularized, expressed exons with an Sn of 0.884, Sp of 0.900, ACC of 0.892, MCC of 0.784, respectively. The identification results suggest that conformational and thermodynamic properties in the flanking introns are closely related to the formation of circRNAs. Datasets and the tool involved in this paper are all available at https://sourceforge.net/projects/predicircrnatool/files/. (C) 2016 Elsevier Ltd. All rights reserved.

Keyword :

Support vector machine Competing endogenous RNA Circular RNA 10-Fold cross-validation

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GB/T 7714 Liu, Ze , Han, Jiuqiang , Lv, Hongqiang et al. Computational identification of circular RNAs based on conformational and thermodynamic properties in the flanking introns [J]. | COMPUTATIONAL BIOLOGY AND CHEMISTRY , 2016 , 61 : 221-225 .
MLA Liu, Ze et al. "Computational identification of circular RNAs based on conformational and thermodynamic properties in the flanking introns" . | COMPUTATIONAL BIOLOGY AND CHEMISTRY 61 (2016) : 221-225 .
APA Liu, Ze , Han, Jiuqiang , Lv, Hongqiang , Liu, Jun , Liu, Ruiling . Computational identification of circular RNAs based on conformational and thermodynamic properties in the flanking introns . | COMPUTATIONAL BIOLOGY AND CHEMISTRY , 2016 , 61 , 221-225 .
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A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology EI SCIE PubMed Scopus
期刊论文 | 2016 , 16 (9) | SENSORS | IF: 2.677
WoS CC Cited Count: 1
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Abstract :

Surface defect detection and dimension measurement of automotive bevel gears by manual inspection are costly, inefficient, low speed and low accuracy. In order to solve these problems, a synthetic bevel gear quality inspection system based on multi-camera vision technology is developed. The system can detect surface defects and measure gear dimensions simultaneously. Three efficient algorithms named Neighborhood Average Difference (NAD), Circle Approximation Method (CAM) and Fast Rotation-Position (FRP) are proposed. The system can detect knock damage, cracks, scratches, dents, gibbosity or repeated cutting of the spline, etc. The smallest detectable defect is 0.4 mm x 0.4 mm and the precision of dimension measurement is about 40-50 m. One inspection process takes no more than 1.3 s. Both precision and speed meet the requirements of real-time online inspection in bevel gear production.

Keyword :

multi-camera vision dimension measurement defect detection bevel gear

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GB/T 7714 Liu, Ruiling , Zhong, Dexing , Lyu, Hongqiang et al. A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology [J]. | SENSORS , 2016 , 16 (9) .
MLA Liu, Ruiling et al. "A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology" . | SENSORS 16 . 9 (2016) .
APA Liu, Ruiling , Zhong, Dexing , Lyu, Hongqiang , Han, Jiuqiang . A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology . | SENSORS , 2016 , 16 (9) .
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