English Version

  • 邹权,电子科技大学基础与前沿研究院教授,博士研究生导师,IEEE高级会员,ACM高级会员,CCF杰出会员。
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  • 研究方向:

  • 利用并行/高性能计算解决生物信息学问题
  • 利用机器学习方法解决生物信息学问题
  • 生物信息学应用问题

  • 主持项目:

    国家自然科学基金优秀青年基金    生物信息处理与分析
    四川省杰出青年科技人才项目    大规模生物序列分类和聚类方法研究
    国家自然科学基金面上项目    利用多序列比对指导16s rRNA的OTU聚类
    国家自然科学基金面上项目    基于MapReduce的非编码RNA“从头预测”识别方法研究 (已结题)
    国家自然科学基金青年基金    基于投票机制的非编码RNA“从头预测”识别方法研究 (已结题)
    福建省自然科学基金面上项目    转录组数据中的microRNA和SNP挖掘方法研究 (已结题)


  • Editor-in-Chief of Current Bioinformatics
  • Associate Editor of IEEE Access, Frontiers in Genetics, Frontiers in Plant Science
  • Editorial Board Member of Computers in Biology and Medicine, Computational Biology and Chemistry, Scientific Report, Interdisciplinary Sciences--Computational Life Sciences, Genes, Frontiers in Bioscience Landmark, Briefings in Functional Genomics
  • Guest Associate Editor of PLoS Computational Biology

  • 出版著作:

  • Jon Galloway, Phil Haack, Brad Wilson等著. 孙远帅, 邹权译. ASP.NET MVC 4高级编程(第4版).清华大学出版社. 2013.8 ISBN:9787302330035
  • 系统生物学的网络分析方法. 邹权, 陈启安, 曾湘祥, 刘向荣 编著. 西安电子科技大学出版社. 2015.6. ISBN:9787560635385
  • Quan Zou (Ed.) Special Protein Molecules Computational Identification. MDPI. St. Alban-Anlage 66. Basel, Switzerland. ISBN: 9783038970439 (Pbk) 9783038970446 (PDF) doi:10.3390/books978-3-03897-044-6
  • Xiangxiang Zeng, Alfonso Rodríguez-Patón and Quan Zou (Eds.) Molecular Computing and Bioinformatics. MDPI. St. Alban-Anlage 66. Basel, Switzerland. ISBN 978-3-03921-195-1 (Pbk) ISBN 978-3-03921-196-8 (PDF) doi:10.3390/books978-3-03921-196-8

  • 特约报告:
  • 生物信息学中的不确定性和分类问题. CRSSC-CWI-CGrC2014青年学者论坛. 2014.8.7. 昆明. PPT
  • Machine learning and computational problems in genome-wide association study. CAAI机器学习专委会首届青年学者交流会. 2014.8.15. 南昌 PPT
  • Reconstructing phylogenetic trees for ultra-large unaligned DNA sequences via with Hadoop. The 9th International Conference on Systems Biology (ISB 2015). 2015.8.21. 洛阳 PPT
  • Computational prediction of miRNA and miRNA-disease relationship. 2015 Asian Conference on Membrane Computing (ACMC2015). 2015.11.14. 合肥 PPT
  • DNA多序列比对中的算法技术和并行方法. 2016大数据与精准生物医学信息学研讨会. 2016.3.26. 上海. PPT
  • Hierarchical learning and high dimensionality problems in bioinformatics. 中国人工智能学会机器学习专委会青年学者论坛. 2017.9.8. 西安 PPT
  • 基因序列的比对、挖掘和功能分析. 第二届中国计算机学会生物信息学会议. 2017.10.14. 长沙. PPT
  • 新的集成分类、降维策略与生物信息应用. 第九届全国生物信息学与系统生物学学术大会. 2020.9.28. 上海. PPT

  • 代表论文:

    1. A comprehensive overview and evaluation of circular RNA detection tools. PLoS Computational Biology. 2017,13(6): e1005420 (SCI, IF2017=3.955, PMID: 28594838) (data and codes)(BibTeX, EndNote)
    2. Details in the evaluation of circular RNA detection tools: Reply to Chen and Chuang. PLoS Computational Biology. 2019, 15(4): e1006916 (SCI, IF2017=3.955, PMID: 31022173)
    3. Gene2vec: Gene Subsequence Embedding for Prediction of Mammalian N6‐Methyladenosine Sites from mRNA. RNA. 2019, 25(2): 205-218 (SCI, IF2017=4.490, PMID: 30425123) (web server)(BibTeX, EndNote)

    4. HAlign: Fast Multiple Similar DNA/RNA Sequence Alignment Based on the Centre Star Strategy. Bioinformatics. 2015,31(15): 2475-2481. (SCI, IF2017=5.481, PMID: 25812743) (Software)(该软件被OMICTOOLS推荐)(BibTeX, EndNote)
    5. Basic polar and hydrophobic properties are the main characteristics that affect the binding of transcription factors to methylation sites. Bioinformatics. 2020,36(15):4263-4268 (Supplementary data)
    6. PEPred-Suite: improved and robust prediction of therapeutic peptides using adaptive feature representation learning. Bioinformatics. 2019, 35(21):4272-4280. (SCI, IF2017=5.481, PMID:30994882) (web server)
    7. PPTPP: A novel therapeutic peptide prediction method using physicochemical property encoding and adaptive feature representation learning. Bioinformatics. 2020, 36(13): 3982-3987 (codes and data)
    8. Tumor Origin Detection with Tissue-Specific miRNA and DNA methylation Markers. Bioinformatics. 2018, 34(3): 398-406. (SCI, IF2017=5.481, PMID:29028927) (web server) High impact research from Bioinformatics (BibTeX, EndNote)
    9. Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species. Bioinformatics. 2019, 35(8): 1326-1333. (SCI, IF2017=5.481, PMID: 30239627)(web server)
    10. O-GlcNAcPRED-II: an integrated classification algorithm for identifying O-GlcNAcylation sites based on fuzzy undersampling and a K-means PCA oversampling technique. Bioinformatics. 2018, 34(12): 2029-2036. (SCI, IF2017=5.481, PMID:29420699)(web server)(该软件被OMICTOOLS推荐)(BibTeX, EndNote)
    11. Prediction of potential disease-associated microRNAs using structural perturbation method. Bioinformatics. 2018, 34(14): 2425-2432.(SCI, IF2017=5.481, PMID:29490018)(codes)(BibTeX, EndNote)
    12. 4mCPred: Machine Learning Methods for DNA N4-methylcytosine sites Prediction. Bioinformatics. 2019, 35(4): 593-601 (SCI, IF2017=5.481, PMID: 30052767)(web server)(该软件被OMICTOOLS推荐)
    13. StackCPPred: A Stacking and Pairwise Energy Content based Prediction of Cell-Penetrating Peptides and Their Uptake Efficiency. Bioinformatics. 2020, 36(10):3028-3034. (code)
    14. Iterative feature representations improve N4-methylcytosine site prediction. Bioinformatics. 2019, 35(23): 4930-4937. (SCI, IF2017=5.481, PMID:31099381)(web server)
    15. PaGeFinder: Quantitative Identification of Spatiotemporal Pattern Genes. Bioinformatics. 2012, 28(11):1544-1545. (SCI, IF2017=5.481, PMID:22492640)(Web Server)(BibTeX, EndNote)
    16. BP4RNAseq: a babysitter package for retrospective and newly generated RNA-seq data analyses using both alignment-based and alignment-free quantification methods. Bioinformatics. 2021, 37(9):1319-1321. (codes)
    17. Identification of Sub-Golgi Protein Localization by Use of Deep Representation Learning Features. Bioinformatics. DOI:10.1093/bioinformatics/btaa1074 (web server)

    18. Sequence clustering in bioinformatics: an empirical study. Briefings in Bioinformatics. 2020,21(1): 1-10 (SCI, IF2017=6.302, PMID: 30239587)(data) Highly Cited Articles from Briefings in Bioinformatics
    19. Revisiting genome-wide association studies from statistical modelling to machine learning. Briefings in Bioinformatics. 2021, 22(4): bbaa263
    20. An in silico approach to identification, categorization and prediction of nucleic acid binding proteins. Briefings in Bioinformatics. 2021,22(3):bbaa171 (web sites)
    21. Survey of MapReduce Frame Operation in Bioinformatics. Briefings in Bioinformatics. 2014,15(4): 637-647. (SCI, IF2017=6.302, PMID: 23396756)(BibTeX, EndNote)
    22. Integrative approaches for predicting microRNA function and prioritizing disease-related microRNA using biological interaction networks. Briefings in Bioinformatics. 2016,17(2):193-203.(SCI, IF2017=6.302, PMID:26059461)(BibTeX, EndNote)
    23. Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms. Briefings in Bioinformatics. 2020,21(1): 106-119. (SCI, IF2017=6.302, PMID:30383239)(web server)
    24. HITS-PR-HHblits: Protein remote homology detection by combining pagerank and hyperlink-induced topic search. Briefings in Bioinformatics. 2020,21(1): 298-308.(SCI, IF2017=6.302, PMID:30403770)(web server)
    25. Computational methods for identifying the critical nodes in biological networks. Briefings in Bioinformatics. 2020, 21(2): 486-497.(SCI, IF2017=6.302, PMID:30753282)
    26. Transcription factors-DNA interactions in rice: identification and verification. Briefings in Bioinformatics. 2020, 21(3): 946-956. (SCI, IF2017=6.302, PMID:31091308)
    27. Clustering and Classification Methods for Single-cell RNA-sequencing Data. Briefings in Bioinformatics. 2020, 21(4): 1196-1208(SCI, IF2017=6.302, PMID:31271412)
    28. DeepATT: a hybrid category attention neural network for identifying functional effects of DNA sequences. Briefings in Bioinformatics. 2021,22(3):bbaa159. (codes)
    29. Critical evaluation of web-based prediction tools for human protein subcellular localization. Briefings in Bioinformatics. 2020,21(5):1628-1640. (web server)
    30. Predicting Disease-associated Circular RNAs Using Deep Forests Combined with Positive-Unlabeled Learning Methods. Briefings in Bioinformatics. 2020, 21(4): 1425-1436.(data and code)
    31. EP3: An ensemble predictor that accurately identifies type III secreted effectors. Briefings in Bioinformatics. 2021,22(2):1918-1928. (web site)
    32. A Spectral Clustering with Self-weighted Multiple Kernel Learning Method for single-cell RNA-seq Data. Briefings in Bioinformatics. 2021, 22(4): bbaa216. (codes)
    33. Goals and Approaches for Each Processing Step for Single-Cell RNA Sequencing Data. Briefings in Bioinformatics. 2021, 22(4): bbaa314. (codes)
    34. VPTMdb: a viral post-translational modification database. Briefings in Bioinformatics. 2021, 22(4): bbaa251. (web sites)
    35. Application of Learning to Rank in Bioinformatics Tasks. Briefings in Bioinformatics. Doi:10.1093/bib/bbaa394
    36. SubLocEP: A novel ensemble predictor of subcellular localization of eukaryotic mRNA based on machine learning. Briefings in Bioinformatics. doi:10.1093/bib/bbaa401. (web site)
    37. GutBalance: a server for the human gut microbiome-based disease prediction and biomarker discovery with compositionality addressed. Briefings in Bioinformatics. doi: 10.1093/bib/bbaa436. (web site)
    38. Prediction of RNA-binding protein and alternative splicing event associations during epithelial-mesenchymal transition based on inductive matrix completion. Briefings in Bioinformatics. Doi: 10.1093/bib/bbaa440. (codes)
    39. Anti-Cancer Peptide Prediction with Deep Representation Learning Features. Briefings in Bioinformatics. DOI: 10.1093/bib/bbab008. (codes)
    40. A comprehensive overview and critical evaluation of gene regulatory network inference technologies. Briefings in Bioinformatics. Doi: 10.1093/bib/bbab009
    41. A Comprehensive Review of the Imbalance Classification of Protein Post-Translational Modifications. Briefings in Bioinformatics. Doi: 10.1093/bib/bbab089. (web site)
    42. DisBalance: a platform to automatically build balance-based disease prediction models and discover microbial biomarkers from microbiome data. Briefings in Bioinformatics. Doi: 10.1093/bib/bbab094 (web)
    43. Critical downstream analysis steps for single-cell RNA sequencing data. Briefings in Bioinformatics. Doi: 10.1093/bib/bbab105 (codes)
    44. MMFGRN: a multi-source multi-model fusion method for Gene Regulatory Network reconstruction. Briefings in Bioinformatics. DOI:10.1093/bib/bbab166. (codes and data)
    45. The accurate prediction and characterization of cancerlectin by a combined machine learning and GO analysis. Briefings in Bioinformatics. Doi:10.1093/bib/bbab227
    46. High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning method. Briefings in Bioinformatics. Doi:10.1093/bib/bbab273
    47. Matrix factorization-based data fusion for the prediction of RNA-binding protein and alternative splicing event associations during epithelial-mesenchymal transition. Briefings in Bioinformatics. Doi:10.1093/bib/bbab332. (codes)

    48. Decision Tree for Sequences. IEEE Transactions on Knowledge and Data Engineering. Doi: 10.1109/TKDE.2021.3075023 (codes)
    49. SgRNA-RF: identification of SgRNA on-target activity with imbalanced datasets. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Accepted. (web server)
    50. Fast prediction of protein methylation sites using a sequence-based feature selection technique. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2019,16(4):1264-1273. (SCI, IF2015=1.609, PMID:28222000)(web server)(该软件被OMICTOOLS推荐)
    51. Prediction and validation of disease genes using HeteSim Scores. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2017, 14(3):687-695. (SCI, IF2017=2.428, PMID:26890920)(Codes and Data)(BibTeX, EndNote)
    52. Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2017, 14(4): 905-915.(Web Server)(SCI, IF2015=1.609, PMID:27076459)(BibTeX, EndNote)
    53. Improved and Promising Identification of Human MicroRNAs by Incorporating a High-quality Negative Set. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2014, 11(1):192-201 (SCI, IF2017=2.428, PMID: 24216114)(Software)(BibTeX, EndNote)
    54. Protein Complexes Identification with Family-Wise Error Rate Control. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2020, 17(6): 2062-2073. (SCI, IF2017=2.428, PMID:31027047)
    55. Significance-Based Essential Protein Discovery. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Doi:10.1109/TCBB.2020.3004364
    56. Advanced machine learning techniques for bioinformatics. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2019,16(4):1182-1183(SCI, IF2017=2.428)
    57. CRCF: A Method of Identifying Secretory Proteins of Malaria Parasites. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Accepted. (web server)
    58. PhosPred-RF: a novel sequence-based predictor for phosphorylation sites using sequential information only. IEEE Transactions on NanoBioscience. 2017, 16(4): 240-247. (SCI, IF2017=2.158, PMID:28166503) (web server)(BibTeX, EndNote)
    59. Investigating maize yield-related genes in multiple omics interaction network data. IEEE Transactions on NanoBioscience. 2020, 19(1): 142-151 (SCI, IF2017=2.158, PMID:31170079)

    60. Prediction of bio-sequence modifications and the associations with diseases. Briefings in Functional Genomics. 2021, 20(1): 1-18 (data)Editor's Choice
    61. CPPred-RF: a sequence-based predictor for identifying cell-penetrating peptides and their uptake efficiency. Journal of Proteome Research.2017, 16(5):2044-2053. (SCI, IF2018=3.780)(web server)(入选ACS出版社庆祝中科院建院70周年Highlight文章)
    62. Prediction of human protein subcellular localization using deep learning. Journal of Parallel and Distributed Computing. 2018, 117: 212-217 (SCI, IF2017=1.815) most cited articles in this journal (BibTeX, EndNote)
    63. Review and comparative analysis of machine learning-based phage virion protein identification methods. BBA - Proteins and Proteomics. 2020,1868(6):140406 Featured in the BBA Collection on Viruses
    64. Multiple Sequence Alignment Based on a Suffix Tree and Center-Star Strategy: A Linear Method for Multiple Nucleotide Sequence Alignment on Spark Parallel Framework. Journal of Computational Biology. 2017, 24(12): 1230-1242 (SCI, IF2017=1.191, PMID: 29116822) (codes)(该软件被OMICTOOLS推荐)(BibTeX, EndNote)


  • 2019年度福建省自然科学奖三等奖(排名第1)
  • 第十届吴文俊人工智能自然科学奖二等奖(排名第2,2020年)
  • 2019年度教育部自然科学奖二等奖(排名第2)
  • 2020年度黑龙江省高校科学技术奖一等奖(排名第2)
  • 2013年度厦门大学第七届高等教育教学成果二等奖(排名第5)
  • 第十三批四川省学术和技术带头人(自然科学)
  • 四川省高层次人才计划
  • 科睿唯安(Clarivate Analytics)“全球高被引学者”(2018, 2019, 2020)
  • 爱思唯尔2020中国高被引学者 (计算机科学与技术领域)
  • Global Peer Review Awards (Top 1% in Biology and Biochemistry, Cross-Field) Powered by Publons
  • 2017年单年引用全球排名第40774名,其中生物信息学领域全球第184名,国内第5名(参考论文PLoS Biology 2019, 17(8): e3000384表S2)
  • 截止2019年,引用全球排名109706名,其中生物信息学领域全球第346名,国内第14名,入选全球前2%;2019年单年引用全球排名第10929名,其中生物信息学领域全球第38名,国内第3名(参考论文PLoS Biology 2020, 18(10): e3000918表S6和S7)
  • Guide2Research计算机学科中国区学者排名第104名,世界第2149名(2021年2月数据)
  • 《Frontiers of Computer Science》2019-2020年度“优秀青年AE”

  • 学术软件


  • 厦门大学
  • 天津大学
  • 电子科技大学

  • 联系方式:

  • Email: zouquan(a)nclab.net(为防止垃圾邮件,请把(a)换成@)
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  • 最后修改时间:2021.8.2