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:


    国家自然科学基金重点项目    面向单细胞RNA测序数据的深度迁移模型与细胞通信网络研究
    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)

    学术兼职:

  • 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. 2020, 36(24):5600-5609 (web server)
    18. DeepAc4C: A convolutional neural network model with hybrid features composed of physico-chemical patterns and distributed representation information for identification of N4 acetylcytidine in mRNA. Bioinformatics. Doi: 10.1093/bioinformatics/btab611. (web server)

    19. 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
    20. Revisiting genome-wide association studies from statistical modelling to machine learning. Briefings in Bioinformatics. 2021, 22(4): bbaa263
    21. An in silico approach to identification, categorization and prediction of nucleic acid binding proteins. Briefings in Bioinformatics. 2021,22(3):bbaa171 (web sites)
    22. Survey of MapReduce Frame Operation in Bioinformatics. Briefings in Bioinformatics. 2014,15(4): 637-647. (SCI, IF2017=6.302, PMID: 23396756)(BibTeX, EndNote)
    23. 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)
    24. 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)
    25. 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)
    26. Computational methods for identifying the critical nodes in biological networks. Briefings in Bioinformatics. 2020, 21(2): 486-497.(SCI, IF2017=6.302, PMID:30753282)
    27. Transcription factors-DNA interactions in rice: identification and verification. Briefings in Bioinformatics. 2020, 21(3): 946-956. (SCI, IF2017=6.302, PMID:31091308)
    28. Clustering and Classification Methods for Single-cell RNA-sequencing Data. Briefings in Bioinformatics. 2020, 21(4): 1196-1208(SCI, IF2017=6.302, PMID:31271412)
    29. DeepATT: a hybrid category attention neural network for identifying functional effects of DNA sequences. Briefings in Bioinformatics. 2021,22(3):bbaa159. (codes)
    30. Critical evaluation of web-based prediction tools for human protein subcellular localization. Briefings in Bioinformatics. 2020,21(5):1628-1640. (web server)
    31. 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)
    32. EP3: An ensemble predictor that accurately identifies type III secreted effectors. Briefings in Bioinformatics. 2021,22(2):1918-1928. (web site)
    33. A Spectral Clustering with Self-weighted Multiple Kernel Learning Method for single-cell RNA-seq Data. Briefings in Bioinformatics. 2021, 22(4): bbaa216. (codes)
    34. Goals and Approaches for Each Processing Step for Single-Cell RNA Sequencing Data. Briefings in Bioinformatics. 2021, 22(4): bbaa314. (codes)
    35. VPTMdb: a viral post-translational modification database. Briefings in Bioinformatics. 2021, 22(4): bbaa251. (web sites)
    36. Application of Learning to Rank in Bioinformatics Tasks. Briefings in Bioinformatics. 2021, 22(5):bbaa394
    37. SubLocEP: A novel ensemble predictor of subcellular localization of eukaryotic mRNA based on machine learning. Briefings in Bioinformatics. 2021, 22(5): bbaa401. (web site)
    38. GutBalance: a server for the human gut microbiome-based disease prediction and biomarker discovery with compositionality addressed. Briefings in Bioinformatics.2021, 22(5):bbaa436. (web site)
    39. Prediction of RNA-binding protein and alternative splicing event associations during epithelial-mesenchymal transition based on inductive matrix completion. Briefings in Bioinformatics. 2021, 22(5): bbaa440. (codes)
    40. Anti-Cancer Peptide Prediction with Deep Representation Learning Features. Briefings in Bioinformatics. 2021, 22(5): bbab008. (codes)
    41. A comprehensive overview and critical evaluation of gene regulatory network inference technologies. Briefings in Bioinformatics. 2021,22(5):bbab009
    42. A Comprehensive Review of the Imbalance Classification of Protein Post-Translational Modifications. Briefings in Bioinformatics. 2021, 22(5):bbab089. (web site)
    43. DisBalance: a platform to automatically build balance-based disease prediction models and discover microbial biomarkers from microbiome data. Briefings in Bioinformatics. 2021, 22(5):bbab094 (web)
    44. Critical downstream analysis steps for single-cell RNA sequencing data. Briefings in Bioinformatics. 2021,22(5):bbab105 (codes)
    45. MMFGRN: a multi-source multi-model fusion method for Gene Regulatory Network reconstruction. Briefings in Bioinformatics. DOI:10.1093/bib/bbab166. (codes and data)
    46. The accurate prediction and characterization of cancerlectin by a combined machine learning and GO analysis. Briefings in Bioinformatics. Doi:10.1093/bib/bbab227
    47. High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning method. Briefings in Bioinformatics. Doi:10.1093/bib/bbab273
    48. 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)
    49. A comparison of deep learning-based pre-processing and clustering approaches for single-cell RNA sequencing data. Briefings in Bioinformatics. DOI: 10.1093/bib/bbab345
    50. Characterizing Viral circRNAs and their Application in Identifying CircRNAs in Viruses. Briefings in Bioinformatics. Accepted. (web server)

    51. Decision Tree for Sequences. IEEE Transactions on Knowledge and Data Engineering. Doi: 10.1109/TKDE.2021.3075023 (codes)
    52. SgRNA-RF: identification of SgRNA on-target activity with imbalanced datasets. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Accepted. (web server)
    53. 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)
    54. 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)
    55. 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)
    56. 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)
    57. 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)
    58. Significance-Based Essential Protein Discovery. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Doi:10.1109/TCBB.2020.3004364
    59. Advanced machine learning techniques for bioinformatics. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2019,16(4):1182-1183(SCI, IF2017=2.428)
    60. CRCF: A Method of Identifying Secretory Proteins of Malaria Parasites. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Accepted. (web server)
    61. 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)
    62. 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)

    63. Prediction of bio-sequence modifications and the associations with diseases. Briefings in Functional Genomics. 2021, 20(1): 1-18 (data)Editor's Choice
    64. 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)
    65. 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)
    66. 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
    67. 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)

    所获奖励:


  • 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


    培养学生:


  • 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@)
  • QQ:(同事请加32400920) (学生请加1020628735)


  • 最后修改时间:2021.9.22
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