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Xin R, Cheng Q, Chi X, Feng X, Zhang H, Wang Y, Duan M, Xie T, Song X, Yu Q, Fan Y, Huang L, Zhou F. Computational Characterization of Undifferentially Expressed Genes with Altered Transcription Regulation in Lung Cancer. Genes (Basel) 2023; 14:2169. [PMID: 38136991 PMCID: PMC10742656 DOI: 10.3390/genes14122169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2023] [Revised: 11/19/2023] [Accepted: 11/27/2023] [Indexed: 12/24/2023] Open
Abstract
A transcriptome profiles the expression levels of genes in cells and has accumulated a huge amount of public data. Most of the existing biomarker-related studies investigated the differential expression of individual transcriptomic features under the assumption of inter-feature independence. Many transcriptomic features without differential expression were ignored from the biomarker lists. This study proposed a computational analysis protocol (mqTrans) to analyze transcriptomes from the view of high-dimensional inter-feature correlations. The mqTrans protocol trained a regression model to predict the expression of an mRNA feature from those of the transcription factors (TFs). The difference between the predicted and real expression of an mRNA feature in a query sample was defined as the mqTrans feature. The new mqTrans view facilitated the detection of thirteen transcriptomic features with differentially expressed mqTrans features, but without differential expression in the original transcriptomic values in three independent datasets of lung cancer. These features were called dark biomarkers because they would have been ignored in a conventional differential analysis. The detailed discussion of one dark biomarker, GBP5, and additional validation experiments suggested that the overlapping long non-coding RNAs might have contributed to this interesting phenomenon. In summary, this study aimed to find undifferentially expressed genes with significantly changed mqTrans values in lung cancer. These genes were usually ignored in most biomarker detection studies of undifferential expression. However, their differentially expressed mqTrans values in three independent datasets suggested their strong associations with lung cancer.
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Affiliation(s)
- Ruihao Xin
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (R.X.); (Y.W.); (M.D.); (L.H.)
- Jilin Institute of Chemical Technology, College of Information and Control Engineering, Jilin 132000, China; (Q.C.); (X.C.); (H.Z.)
| | - Qian Cheng
- Jilin Institute of Chemical Technology, College of Information and Control Engineering, Jilin 132000, China; (Q.C.); (X.C.); (H.Z.)
| | - Xiaohang Chi
- Jilin Institute of Chemical Technology, College of Information and Control Engineering, Jilin 132000, China; (Q.C.); (X.C.); (H.Z.)
| | - Xin Feng
- School of Science, Jilin Institute of Chemical Technology, Jilin 132000, China;
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130012, China;
| | - Hang Zhang
- Jilin Institute of Chemical Technology, College of Information and Control Engineering, Jilin 132000, China; (Q.C.); (X.C.); (H.Z.)
| | - Yueying Wang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (R.X.); (Y.W.); (M.D.); (L.H.)
| | - Meiyu Duan
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (R.X.); (Y.W.); (M.D.); (L.H.)
| | - Tunyang Xie
- Centre for Mathematical Sciences, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK;
| | - Xiaonan Song
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Software, Jilin University, Changchun 130012, China;
| | - Qiong Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun 130012, China;
| | - Yusi Fan
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Software, Jilin University, Changchun 130012, China;
| | - Lan Huang
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (R.X.); (Y.W.); (M.D.); (L.H.)
| | - Fengfeng Zhou
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun 130012, China; (R.X.); (Y.W.); (M.D.); (L.H.)
- School of Biology and Engineering, Guizhou Medical University, Guiyang 550025, China
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Li X, Lv X, Li H, Zhang G, Long Y, Li K, Fan Y, Jin D, Zhou F, Liu H. Undifferentially Expressed CXXC5 as a Transcriptionally Regulatory Biomarker of Breast Cancer. Adv Biol (Weinh) 2023; 7:e2300189. [PMID: 37423953 DOI: 10.1002/adbi.202300189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 06/17/2023] [Indexed: 07/11/2023]
Abstract
This work hypothesizes that some genes undergo radically changed transcription regulations (TRs) in breast cancer (BC), but don't show differential expressions for unknown reasons. The TR of a gene is quantitatively formulated by a regression model between the expression of this gene and multiple transcription factors (TFs). The difference between the predicted and real expression levels of a gene in a query sample is defined as the mqTrans value of this gene, which quantitatively reflects its regulatory changes. This work systematically screens the undifferentially expressed genes with differentially expressed mqTrans values in 1036 samples across five datasets and three ethnic groups. This study calls the 25 genes satisfying the above hypothesis in at least four datasets as dark biomarkers, and the strong dark biomarker gene CXXC5 (CXXC Finger Protein 5) is even supported by all the five independent BC datasets. Although CXXC5 does not show differential expressions in BC, its transcription regulations show quantitative associations with BCs in diversified cohorts. The overlapping long noncoding RNAs (lncRNAs) may have contributed their transcripts to the expression miscalculations of dark biomarkers. The mqTrans analysis serves as a complementary view of the transcriptome-based detections of biomarkers that are ignored by many existing studies.
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Affiliation(s)
- Xue Li
- School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 550025, China
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Xiaoying Lv
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Haijun Li
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Gongyou Zhang
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Yaohang Long
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
| | - Kewei Li
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Yusi Fan
- College of Software, Jilin University, Changchun, 130012, China
| | - Dawei Jin
- Research Institute of Guizhou Huada Life Big Data, Guiyang, Guizhou, 550025, China
| | - Fengfeng Zhou
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
- College of Computer Science and Technology, Jilin University, Changchun, 130012, China
| | - Hongmei Liu
- School of Biology and Engineering, Guizhou Medical University, Guiyang, 550025, China
- Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang, Guizhou, 550025, China
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China
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Duan M, Liu Y, Zhao D, Li H, Zhang G, Liu H, Wang Y, Fan Y, Huang L, Zhou F. Gender-specific dysregulations of nondifferentially expressed biomarkers of metastatic colon cancer. Comput Biol Chem 2023; 104:107858. [PMID: 37058814 DOI: 10.1016/j.compbiolchem.2023.107858] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/12/2023] [Accepted: 03/29/2023] [Indexed: 04/16/2023]
Abstract
Colon cancer is a common cancer type in both sexes and its mortality rate increases at the metastatic stage. Most studies exclude nondifferentially expressed genes from biomarker analysis of metastatic colon cancers. The motivation of this study is to find the latent associations of the nondifferentially expressed genes with metastatic colon cancers and to evaluate the gender specificity of such associations. This study formulates the expression level prediction of a gene as a regression model trained for primary colon cancers. The difference between a gene's predicted and original expression levels in a testing sample is defined as its mqTrans value (model-based quantitative measure of transcription regulation), which quantitatively measures the change of the gene's transcription regulation in this testing sample. We use the mqTrans analysis to detect the messenger RNA (mRNA) genes with nondifferential expression on their original expression levels but differentially expressed mqTrans values between primary and metastatic colon cancers. These genes are referred to as dark biomarkers of metastatic colon cancer. All dark biomarker genes were verified by two transcriptome profiling technologies, RNA-seq and microarray. The mqTrans analysis of a mixed cohort of both sexes could not recover gender-specific dark biomarkers. Most dark biomarkers overlap with long non-coding RNAs (lncRNAs), and these lncRNAs might have contributed their transcripts to calculating the dark biomarkers' expression levels. Therefore, mqTrans analysis serves as a complementary approach to identify dark biomarkers generally ignored by conventional studies, and it is essential to separate the female and male samples into two analysis experiments. The dataset and mqTrans analysis code are available at https://figshare.com/articles/dataset/22250536.
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Affiliation(s)
- Meiyu Duan
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China; School of Biology and Engineering, Guizhou Medical University, Guiyang 550025, Guizhou, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Yaqing Liu
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Dong Zhao
- School of Biology and Engineering, Guizhou Medical University, Guiyang 550025, Guizhou, China
| | - Haijun Li
- School of Biology and Engineering, Guizhou Medical University, Guiyang 550025, Guizhou, China
| | - Gongyou Zhang
- School of Biology and Engineering, Guizhou Medical University, Guiyang 550025, Guizhou, China
| | - Hongmei Liu
- School of Biology and Engineering, Guizhou Medical University, Guiyang 550025, Guizhou, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China; Engineering Research Center of Medical Biotechnology, Guizhou Medical University, Guiyang 550025, Guizhou, China
| | - Yueying Wang
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Yusi Fan
- Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China; College of Software, Jilin University, Changchun, Jilin 130012, China.
| | - Lan Huang
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Fengfeng Zhou
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China; School of Biology and Engineering, Guizhou Medical University, Guiyang 550025, Guizhou, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China.
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Xin R, Feng X, Zhang H, Wang Y, Duan M, Xie T, Dong L, Yu Q, Huang L, Zhou F. Seven non-differentially expressed 'dark biomarkers' show transcriptional dysregulation in chronic lymphocytic leukemia. Per Med 2023. [PMID: 36705049 DOI: 10.2217/pme-2022-0123] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Aim: Transcriptional regulation is actively involved in the onset and progression of various diseases. This study used the feature-engineering approach model-based quantitative transcription regulation to quantitatively measure the correlation between mRNA and transcription factors in a reference dataset of chronic lymphocytic leukemia (CLL) transcriptomes. Methods: A comprehensive investigation of transcriptional regulation changes in CLL was conducted using 973 samples in six independent datasets. Results & conclusion: Seven mRNAs were detected to have significantly differential model-based quantitative transcription regulation values but no differential expression between CLL patients and controls. We called these genes 'dark biomarkers' because their original expression levels did not show differential changes in the CLL patients. The overlapping lncRNAs might have contributed their transcripts to the expression miscalculations of these dark biomarkers.
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Affiliation(s)
- Ruihao Xin
- College of Computer Science and Technology & Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China.,College of Information & Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132000, China
| | - Xin Feng
- School of Science, Jilin Institute of Chemical Technology, Jilin,132000, China.,Department of Epidemiology & Biostatistics, School of Public Health, Jilin University, Changchun, 130012, China
| | - Hang Zhang
- College of Information & Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132000, China
| | - Yueying Wang
- College of Computer Science and Technology & Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Meiyu Duan
- College of Computer Science and Technology & Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Tunyang Xie
- Centre for Mathematical Sciences, University of Cambridge, Wilberforce Road, Cambridge, CB3 0WA, UK
| | - Lin Dong
- Department of Epidemiology & Biostatistics, School of Public Health, Jilin University, Changchun, 130012, China
| | - Qiong Yu
- Department of Epidemiology & Biostatistics, School of Public Health, Jilin University, Changchun, 130012, China
| | - Lan Huang
- College of Computer Science and Technology & Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Fengfeng Zhou
- College of Computer Science and Technology & Key Laboratory of Symbolic Computation & Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
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Xu C, Zhang R, Duan M, Zhou Y, Bao J, Lu H, Wang J, Hu M, Hu Z, Zhou F, Zhu W. A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia. MOLECULAR THERAPY - NUCLEIC ACIDS 2022; 28:477-487. [PMID: 35505964 PMCID: PMC9046129 DOI: 10.1016/j.omtn.2022.04.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Accepted: 04/01/2022] [Indexed: 01/19/2023]
Abstract
Immune thrombocytopenia (ITP) is an autoimmune disease with the typical symptom of a low platelet count in blood. ITP demonstrated age and sex biases in both occurrences and prognosis, and adult ITP was mainly induced by the living environments. The current diagnosis guideline lacks the integration of molecular heterogenicity. This study recruited the largest cohort of platelet transcriptome samples. A comprehensive procedure of feature selection, feature engineering, and stacking classification was carried out to detect the ITP biomarkers using RNA sequencing (RNA-seq) transcriptomes. The 40 detected biomarkers were loaded to train the final ITP detection model, with an overall accuracy 0.974. The biomarkers suggested that ITP onset may be associated with various transcribed components, including protein-coding genes, long intergenic non-coding RNA (lincRNA) genes, and pseudogenes with apparent transcriptions. The delivered ITP detection model may also be utilized as a complementary ITP diagnosis tool. The code and the example dataset is freely available on http://www.healthinformaticslab.org/supp/resources.php
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Affiliation(s)
- Chengfeng Xu
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Ruochi Zhang
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Meiyu Duan
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Yongming Zhou
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Jizhang Bao
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Hao Lu
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Jie Wang
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Minghui Hu
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
| | - Zhaoyang Hu
- Fun-Med Pharmaceutical Technology (Shanghai) Co., Ltd., RM. A310, 115 Xinjunhuan Road, Minhang District, Shanghai 201100, China
- Corresponding author Zhaoyang Hu, PhD, Fengneng Pharmaceutical Technology (Shanghai) Co., Ltd., RM. A310, 115 Xinjunhuan Road, Minhang District, Shanghai 201100, China.
| | - Fengfeng Zhou
- College of Computer Science and Technology, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
- Corresponding author Fengfeng Zhou, PhD, College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China.
| | - Wenwei Zhu
- Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China
- Corresponding author Wenwei Zhu, PhD, Department of Hematology, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, 110 Ganhe Road, Hongkou District, Shanghai 200437, China.
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Duan M, Zhang L, Wang Y, Fan Y, Liu S, Yu Q, Huang L, Zhou F. Computational pan-cancer characterization of model-based quantitative transcription regulations dysregulated in regional lymph node metastasis. Comput Biol Med 2021; 135:104571. [PMID: 34166881 DOI: 10.1016/j.compbiomed.2021.104571] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 10/21/2022]
Abstract
Cancer is one of the major causes of mortality worldwide. Regional lymph node metastasis is an important mechanism during the spread of human cancers, in which transcription regulation plays an essential role. This study formulated a regression-model-based quantitative transcription regulation (mqTrans) between one mRNA gene and multiple transcription factors (TFs). Computational pan-cancer screening was carried out to detect the quantitative dysregulation of transcription regulation in the regional lymph node metastasis of 18 cancer types. Only a few metastasis-dysregulated mqTrans models were shared among the cancer types. The mRNA genes of the metastasis-dysregulated mqTrans models were not differentially expressed in regional lymph node metastasis. The experimental data suggested that mqTrans technology provided a complementary approach to the evaluation of transcription regulation mechanisms and may facilitate its quantitative investigation in other phenotypes.
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Affiliation(s)
- Meiyu Duan
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Lei Zhang
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Yueying Wang
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China; Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin Province, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Yusi Fan
- College of Software, Jilin University, Changchun, Jilin, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Shuai Liu
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Qiong Yu
- Department of Epidemiology and Biostatistics, School of Public Health, Jilin University, Changchun, Jilin Province, China
| | - Lan Huang
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Fengfeng Zhou
- College of Computer Science and Technology, Jilin University, Changchun, Jilin, 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China.
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