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2020 – today
- 2021
[j34]Yan Zheng, Yuanke Zhong, Jialu Hu, Xuequn Shang:
SCC: an accurate imputation method for scRNA-seq dropouts based on a mixture model. BMC Bioinform. 22(1): 5 (2021)- 2020
- [j33]Jiajie Peng, Junya Lu, Donghee Hoh, Ayesha S. Dina, Xuequn Shang, David M. Kramer, Jin Chen:
Identifying emerging phenomenon in long temporal phenotyping experiments. Bioinform. 36(2): 568-577 (2020) - [j32]Yang Qi, Yang Guo, Huixin Jiao, Xuequn Shang:
A flexible network-based imputing-and-fusing approach towards the identification of cell types from single-cell RNA-seq data. BMC Bioinform. 21(1): 240 (2020) - [j31]Yuanke Zhong, Jing Li, Junhao He, Yiqun Gao, Jie Liu, Jingru Wang, Xuequn Shang, Jialu Hu:
Twadn: an efficient alignment algorithm based on time warping for pairwise dynamic networks. BMC Bioinform. 21-S(13): 385 (2020) - [j30]Jiajie Peng, Jingyi Li, Xuequn Shang:
A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network. BMC Bioinform. 21-S(13): 394 (2020) - [j29]Bolin Chen, Manting Yang, Li Gao, Tao Jiang, Xuequn Shang:
A functional network construction method to interpret the pathological process of colorectal cancer. Int. J. Data Min. Bioinform. 23(3): 251-264 (2020) - [j28]Samer Abdulateef, Naseer Ahmed Khan, Bolin Chen, Xuequn Shang:
Multidocument Arabic Text Summarization Based on Clustering and Word2Vec to Reduce Redundancy. Inf. 11(2): 59 (2020) - [j27]Samer Abdulateef
, Naseer Ahmed Khan, Bolin Chen, Xuequn Shang:
Machine Learning Based Sentiment Text Classification for Evaluating Treatment Quality of Discharge Summary. Inf. 11(5): 281 (2020) - [j26]Yupei Zhang, Huan Dai, Yue Yun, Shuhui Liu, Andrew Lan, Xuequn Shang:
Meta-knowledge dictionary learning on 1-bit response data for student knowledge diagnosis. Knowl. Based Syst. 205: 106290 (2020) - [c41]Xinpeng Guo, Yanli Lu, Zhilei Yin, Xuequn Shang:
IPMM: Cancer Subtype Clustering Model Based on Multiomics Data and Pathway and Motif Information. ADMA 2020: 560-568 - [c40]Bolin Chen, Li Gao, Xuequn Shang:
A Machine Learning Based Method to Identify Differentially Expressed Genes. ICIC (2) 2020: 21-31 - [c39]Bolin Chen, Teng Wang, Xuequn Shang:
Identification and Analysis of Genes Involved in Stages of Colon Cancer. ICIC (2) 2020: 161-172
2010 – 2019
- 2019
- [j25]Jiajie Peng, Weiwei Hui, Qianqian Li, Bolin Chen, Jianye Hao, Qinghua Jiang, Xuequn Shang, Zhongyu Wei:
A learning-based framework for miRNA-disease association identification using neural networks. Bioinform. 35(21): 4364-4371 (2019) - [j24]Jialu Hu, Jingru Wang, Jianan Lin, Tianwei Liu, Yuanke Zhong, Jie Liu, Yan Zheng, Yiqun Gao, Junhao He, Xuequn Shang:
MD-SVM: a novel SVM-based algorithm for the motif discovery of transcription factor binding sites. BMC Bioinform. 20-S(7): 41-48 (2019) - [j23]Chaima Aouiche, Bolin Chen, Xuequn Shang:
Predicting stage-specific cancer related genes and their dynamic modules by integrating multiple datasets. BMC Bioinform. 20-S(7): 97-107 (2019) - [j22]Jiajie Peng, Xiaoyu Wang, Xuequn Shang:
Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data. BMC Bioinform. 20-S(8): 284:1-284:12 (2019) - [j21]Jialu Hu, Yiqun Gao, Jing Li, Yan Zheng, Jingru Wang, Xuequn Shang:
A novel algorithm based on bi-random walks to identify disease-related lncRNAs. BMC Bioinform. 20-S(18): 569:1-569:11 (2019) - [j20]Jiajie Peng, Guilin Lu, Hansheng Xue, Tao Wang, Xuequn Shang:
TS-GOEA: a web tool for tissue-specific gene set enrichment analysis based on gene ontology. BMC Bioinform. 20-S(18): 572:1-572:7 (2019) - [j19]Shiquan Sun, Yabo Chen, Yang Liu, Xuequn Shang:
A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data. BMC Syst. Biol. 13-S(2): 28:1-28:8 (2019) - [j18]Hansheng Xue, Jiajie Peng, Xuequn Shang:
Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO. BMC Syst. Biol. 13-S(2): 34:1-34:12 (2019) - [j17]Yang Guo, Xuequn Shang, Zhanhuai Li:
Identification of cancer subtypes by integrating multiple types of transcriptomics data with deep learning in breast cancer. Neurocomputing 324: 20-30 (2019) - [c38]Hansheng Xue, Jiajie Peng, Xuequn Shang:
Towards Gene Function Prediction via Multi-Networks Representation Learning. AAAI 2019: 10069-10070 - [c37]Bolin Chen, Manting Yang, Li Gao, Xuequn Shang:
Identifying functional evolution processes according to the pathological stages of colorectal cancer. BIBM 2019: 193-196 - [c36]Yuanke Zhong, Jing Li, Jie Liu, Yan Zheng, Xuequn Shang, Jialu Hu:
Deep learning enables accurate alignment of single cell RNA-seq data. BIBM 2019: 778-781 - [c35]Shiquan Sun, Yang Liu, Xuequn Shang:
Deep generative autoencoder for low-dimensional embeding extraction from single-cell RNAseq data. BIBM 2019: 1365-1372 - [c34]Jiajie Peng, Lu Han, Xuequn Shang:
A deconvolution method for predicting cell abundance based on single cell RNA-seq data. BIBM 2019: 1769-1773 - [c33]Hansheng Xue, Jiajie Peng, Jiying Li, Xuequn Shang:
Integrating Multi-Network Topology via Deep Semi-supervised Node Embedding. CIKM 2019: 2117-2120 - [c32]Yupei Zhang, Huan Dai, Yue Yun, Xuequn Shang:
Student Knowledge Diagnosis on Response Data via the Model of Sparse Factor Learning. EDM 2019 - [c31]Bolin Chen, Li Gao, Xuequn Shang:
Identifying Differentially Expressed Genes Based on Differentially Expressed Edges. ICIC (2) 2019: 105-115 - [c30]Bolin Chen, Chaima Aouiche, Xuequn Shang:
Integrating Multiple Datasets to Discover Stage-Specific Cancer Related Genes and Stage-Specific Pathways. IWBBIO (1) 2019: 240-250 - 2018
- [j16]Jialu Hu, Yiqun Gao, Junhao He, Yan Zheng, Xuequn Shang:
WebNetCoffee: a web-based application to identify functionally conserved proteins from Multiple PPI networks. BMC Bioinform. 19(1): 422:1-422:6 (2018) - [j15]Jiajie Peng, Weiwei Hui, Xuequn Shang:
Measuring phenotype-phenotype similarity through the interactome. BMC Bioinform. 19-S(5): 65-77 (2018) - [j14]Yang Guo, Shuhui Liu, Zhanhuai Li, Xuequn Shang:
BCDForest: a boosting cascade deep forest model towards the classification of cancer subtypes based on gene expression data. BMC Bioinform. 19-S(5): 118:1-118:13 (2018) - [j13]Jialu Hu, Yiqun Gao, Yan Zheng, Xuequn Shang:
KF-finder: identification of key factors from host-microbial networks in cervical cancer. BMC Syst. Biol. 12(4): 41-48 (2018) - [j12]Jiajie Peng, Xuanshuo Zhang, Weiwei Hui, Junya Lu, Qianqian Li, Shuhui Liu, Xuequn Shang:
Improving the measurement of semantic similarity by combining gene ontology and co-functional network: a random walk based approach. BMC Syst. Biol. 12(S-2): 109-116 (2018) - [j11]Chaima Aouiche, Xuequn Shang, Bolin Chen:
Copy number variation related disease genes. Quant. Biol. 6(2): 99-112 (2018) - [c29]Bo Yang, Shuhui Liu, Shanmin Pang, Chenpai Pang, Xuequn Shang:
Deep Subspace Similarity Fusion for the Prediction of Cancer Subtypes. BIBM 2018: 566-571 - [c28]Yiqun Gao, Jialu Hu, Xuequn Shang:
Identification of lncRNA-disease association using bi-random walks. BIBM 2018: 1249-1255 - [c27]Jiajie Peng, Guilin Lu, Hansheng Xue, Tao Wang, Xuequn Shang:
TSGOE: A web tool for tissue-specific gene ontology enrichment. BIBM 2018: 1266-1269 - [c26]Jialu Hu, Junhao He, Yiqun Gao, Yan Zheng, Xuequn Shang:
NetCoffee2: A Novel Global Alignment Algorithm for Multiple PPI Networks Based on Graph Feature Vectors. ICIC (2) 2018: 241-246 - [c25]Shuhui Liu, Xuequn Shang:
Hierarchical Similarity Network Fusion for Discovering Cancer Subtypes. ISBRA 2018: 125-136 - [c24]Yupei Zhang, Shuhui Liu, Xuequn Shang, Ming Xiang:
Low-Rank Graph Regularized Sparse Coding. PRICAI (1) 2018: 177-190 - [i1]Hansheng Xue, Jiajie Peng, Xuequn Shang:
Deep Feature Learning of Multi-Network Topology for Node Classification. CoRR abs/1809.02394 (2018) - 2017
- [j10]Jiajie Peng, Qianqian Li, Xuequn Shang:
Investigations on factors influencing HPO-based semantic similarity calculation. J. Biomed. Semant. 8-S(1): 61-69 (2017) - [j9]Jiajie Peng, Honggang Wang, Junya Lu, Weiwei Hui, Yadong Wang, Xuequn Shang:
Identifying term relations cross different gene ontology categories. BMC Bioinform. 18(16): 67-74 (2017) - [j8]Jiajie Peng, Hansheng Xue, Yukai Shao, Xuequn Shang, Yadong Wang, Jin Chen:
A novel method to measure the semantic similarity of HPO terms. Int. J. Data Min. Bioinform. 17(2): 173-188 (2017) - [j7]Bo Yang, Xuequn Shang, Shanmin Pang:
Isometric hashing for image retrieval. Signal Process. Image Commun. 59: 117-130 (2017) - [c23]Jialu Hu, Yan Zheng, Xuequn Shang:
MiteFinder: A fast approach to identify miniature inverted-repeat transposable elements on a genome-wide scale. BIBM 2017: 164-168 - [c22]Yang Guo, Shuhui Liu, Zhanhuai Li, Xuequn Shang:
Towards the classification of cancer subtypes by using cascade deep forest model in gene expression data. BIBM 2017: 1664-1669 - [c21]Jiajie Peng, Weiwei Hui, Xuequn Shang:
Measuring phenotype-phenotype similarity through the interactome. BIBM 2017: 1674-1677 - [c20]Bolin Chen, Yuqiong Jin, Xuequn Shang:
Net2Image: A Network Representation Method for Identifying Cancer-Related Genes. ISBRA 2017: 337-343 - 2016
- [j6]Xuequn Shang, Yu Wang, Bolin Chen:
Identifying essential proteins based on dynamic protein-protein interaction networks and RNA-Seq datasets. Sci. China Inf. Sci. 59(7): 070106:1-070106:11 (2016) - [j5]Tao Jiang, Zhanhuai Li, Xuequn Shang, Bolin Chen, Weibang Li, Zhilei Yin:
Constrained query of order-preserving submatrix in gene expression data. Frontiers Comput. Sci. 10(6): 1052-1066 (2016) - [j4]Tao Jiang, Zhanhuai Li, Xuequn Shang, Bolin Chen, Weibang Li:
基因表达数据中局部模式的查询 (Local Pattern Query from Gene Expression Data). 计算机科学 43(7): 191-196 (2016) - [c19]Jiajie Peng, Hansheng Xue, Yukai Shao, Xuequn Shang, Yadong Wang, Jin Chen:
Measuring phenotype semantic similarity using Human Phenotype Ontology. BIBM 2016: 763-766 - [c18]Jiajie Peng, Qianqian Li, Bolin Chen, Jialu Hu, Xuequn Shang:
Analyzing factors involved in the HPO-based semantic similarity calculation. BIBM 2016: 1653-1656 - 2015
- [c17]Bolin Chen, Xuequn Shang, Min Li, Jianxin Wang, Fang-Xiang Wu:
A two-step logistic regression algorithm for identifying individual-cancer-related genes. BIBM 2015: 195-200 - 2014
- [c16]Yang Guo, Xuequn Shang, Qingping Zhu, Mingkui Huang, Zhanhuai Li:
Identification of protein complexes and functional modules in integrated PPI networks. BIBM 2014: 8-15 - 2013
- [c15]Yang Guo, Xuequn Shang, Jing Li, Zhanhuai Li:
Revealing the Causes of Dynamic Change in Protein-Protein Interaction Network. BigData Congress 2013: 189-194 - 2011
- [j3]Miao Wang, Xuequn Shang, Zhanhuai Li:
MFC: Mining Maximal Frequent Dense Subgraphs without Candidate Maintenance in Imbalanced PPI Networks. J. Softw. 6(3): 498-507 (2011) - [c14]Miao Wang, Xuequn Shang, Miao Miao, Zhanhuai Li, Wenbin Liu:
FTCluster: Efficient Mining Fault-Tolerant Biclusters in Microarray Dataset. ICDM Workshops 2011: 1075-1082 - [c13]Miao Wang, Xuequn Shang, Miao Miao, Zhanhuai Li, Wenbin Liu:
MFCluster: Mining Maximal Fault-Tolerant Constant Row Biclusters in Microarray Dataset. WAIM 2011: 181-190 - 2010
- [j2]Miao Wang, Xuequn Shang, Shaohua Zhang, Zhanhuai Li:
Efficient Mining Frequent Closed Discriminative Biclusters by Sample-Growth: The FDCluster Approach. Int. J. Knowl. Discov. Bioinform. 1(4): 69-88 (2010) - [c12]Miao Wang, Xuequn Shang, Jingni Diao, Zhanhuai Li:
WIBE: Mining Frequent Closed Patterns Without Candidate Maintenance in Microarray Dataset. DMIN 2010: 200-205 - [c11]Miao Wang, Xuequn Shang, Shaohua Zhang, Zhanhuai Li:
FDCluster: Mining Frequent Closed Discriminative Bicluster without Candidate Maintenance in Multiple Microarray Datasets. ICDM Workshops 2010: 779-786
2000 – 2009
- 2009
- [c10]Xuequn Shang, Qian Zhao, Zhanhuai Li:
Mining High-Correlation Association Rules for Inferring Gene Regulation Networks. DaWaK 2009: 244-255 - [c9]Xuequn Shang, Zhanhuai Li, Wei Li:
Mining functional associated patterns from biological network data. SAC 2009: 1488-1489 - 2008
- [c8]Miao Wang, Xuequn Shang, Zhanhuai Li:
Sequential Pattern Mining for Protein Function Prediction. ADMA 2008: 652-658 - 2007
- [j1]Xuequn Shang, Kai-Uwe Sattler:
Processing Sequential Patterns in Relational Databases. J. Data Semant. 8: 203-217 (2007) - [c7]Xuequn Shang:
A Survey of Frequent Pattern Mining for Biological Datasets. DMIN 2007: 242-248 - 2005
- [b1]Xuequn Shang:
SQL based frequent pattern mining. Otto-von-Guericke University Magdeburg, Germany, 2005, pp. 1-127 - [c6]Xuequn Shang, Kai-Uwe Sattler:
Processing Sequential Patterns in Relational Databases. DaWaK 2005: 438-447 - [c5]Xuequn Shang, Kai-Uwe Sattler:
Frequent Itemset Mining with Parallel RDBMS. PAKDD 2005: 539-544 - [c4]Xuequn Shang, Kai-Uwe Sattler:
Depth-first frequent itemset mining in relational databases. SAC 2005: 1112-1117 - 2004
- [c3]Xuequn Shang, Kai-Uwe Sattler, Ingolf Geist:
Efficient Frequent Pattern Mining in Relational Databases. LWA 2004: 84-91 - [c2]Xuequn Shang, Kai-Uwe Sattler, Ingolf Geist:
SQL based frequent pattern mining without candidate generation. SAC 2004: 618-619 - [c1]Xuequn Shang, Kai-Uwe Sattler, Ingolf Geist:
SQL Based Frequent Pattern Mining with FP-Growth. INAP/WLP 2004: 32-46
Coauthor Index
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