快捷导航：
研究方向
教育背景
工作经历
教学工作
科研工作
主要荣誉
学术兼职
其它
发表论文

彭珍玲 职称：
副教授
院系：
应用数学中心
电子邮箱：
zhenling@tju.edu.cn
办公地点：
卫津路校区25教B-1102



研究方向 计算生物与生物信息学；机器学习与数据挖掘
教育背景

2001/09 - 2005/06




中国衡阳师范大学，学士








2005/09 - 2008/06




中国湘潭大学，硕士








2010/09 - 2014/06




University of Alberta，Canada，博士







工作经历

2008/09 - 2010.06




贵州工程应用技术学院数学系，助教





2015/09 - 今




天津大学应用数学中心，副教授




教学工作 本科生课程 《概率论》
指导在读硕士生4人》


科研工作

2016/01-2018/12




国家自然科学基金青年基金项目：与RNA、DNA及蛋白质绑定的固有无序片段的分析及预测，主持








2017/01-2018/12




天津大学自主创新基金：计算生物及生物信息学，主持








2011/05-2014/04




加拿大阿尔伯塔省项目：Computational Characterization and Improved Prediction of Protein Disorder with Application in Protein-Protein Interactions，主持








2015/01-2017/12




863项目：生物大数据开发与利用关键技术研究，参与








2016/01-2016/12




国家基金应急管理项目：天津应用数学中心平台建设，参与







主要荣誉

2017/01-2018/12




天津大学北洋*****青年骨干教师计划





2011/05-2014/04




阿尔伯塔省优秀研究生奖学金（Alberta Innovates Graduate Student Scholarship）





2014/05-2014/11




阿尔伯塔大学博士论文奖 （Dissertation Fellowship, University of Alberta）





2005




湖南省优秀毕业生




学术兼职 BMC Bioinformatics，Computational Biology and Chemistry， International Journal of Molecular Sciences等多个SCI期刊的审稿人
其它
发表论文
1.Peng Z, Wang C, Uversky VN and Kurgan L*, Prediction of Disordered RNA, DNA, and Protein Binding Regions Using DisoRDPbind. Methods in Molecular Biology, Springer New York, 2017.

2.Wuyun Q#, Zheng W#, Peng Z and Yang J*. (2016) A large-scale comparative assessment of methods for residue-residue contact prediction. Briefings in Bioinformatics, doi: 10.1093/bib/bbw106.

3.Xia J, Peng Z, Qi D, Mu H and Yang J*. (2016) An ensemble approach to protein fold classification by integration of template-based assignment and support vector machine classifier. Bioinformatics, accepted.

4.Peng Z, Uversky VN and Kurgan L*. (2016) Genes encoding intrinsic disorder in Eukaryota have high GC content. Intrinsically Disordered Proteins, accepted.

5.Peng Z and Kurgan L*. (2015) High-throughput prediction of RNA, DNA and protein binding regions mediated by intrinsic disorder. Nucleic Acids Research, 43(18), e121.

6.Wu Z, Hu G, Yang J, Peng Z, Uversky VN* and Kurgan L*. (2015) In various protein complexes, disordered protomers have large per-residue surface areas and area of protein-, DNA- and RNA-binding interfaces. FEBS Letters, 589(19), 2561-2569.

7.Peng Z, Yan J, Fan X, Mizianty MJ, Xue B, Uversky VN* and Kurgan L*. (2015) Exceptionally abundant exceptions: comprehensive characterization of intrinsic disorder in a thousand proteomes from all domains of life. Cellular and Molecular Life Science. 72(1), 137-151.

8.Peng Z, Oldfield CJ, Xue B, Mizianty MJ, Dunker AK, Kurgan L and Uversky VN. (2014) A creature with a hundred waggly tails: intrinsically disordered proteins in the ribosome. Cellular and Molecular Life Science. 71(8), 1477-1504.

9.Groenendyk J, Peng Z, Dudek E, Fan X, Mizianty MJ, Dufey E, Urra H, Sepulveda D, Rojas-Rivera D, Lim Y, Kim do H, Baretta K, Srikanth S, Gwack Y, Ahnn J, Kaufman RJ, Lee SK, Hetz C, Kurgan L and Michalak M*. (2014) Interplay between PDIA6 and miR-322 controls adaptive response to disrupted endoplasmic reticulum calcium homeostasis. Science Signaling. 7(329), ra54.

10.Peng Z, Sakai Y, Kurgan L, Sokolowski B* and Uversky VN*. (2014) Intrinsic disorder in the BK channel and its interactome. PLoS ONE. 9(4), e94331.

11.Peng Z, Mizianty MJ and Kurgan L*. (2014) Genome-scale prediction of proteins with long intrinsically disordered regions. Proteins: Structure, Function, and Bioinformatics, 82, 145-158.

12.Groenendyk J, Fan X, Peng Z, Ilnytskyy Y, Kurgan L, Michalak M*. (2014) Genome-wide analysis of thapsigargin-induced microRNAs and their targets in NIH3T3 cells. Genomics Data, 2, 325-327.

13.Peng Z, Xue B, Kurgan L* and Uversky VN*. (2013) Resilience of death: intrinsic disorder in proteins involved in the programmed cell death. Cell Death and Differentiation, 20, 1257-1267.

14.Uversky AV, Xue B, Peng Z, Kurgan L and Uversky VN*. (2013) On the intrinsic disorder status of the major players in programmed cell death pathways. F1000 Research, 2, 190.

15.Mizianty MJ, Peng Z and Kurgan L*. (2013) MFDp2 - Accurate Predictor of Disorder in Proteins by Fusion of Disorder Probabilities, Content and Profiles. Intrinsically Disordered Proteins, 1, e24428-1.

16.Peng Z, Mizianty MJ, Xue B, Kurgan L* and Uversky VN*. (2012) More than just tails: intrinsic disorder in histone proteins. Molecular Biosystems, 8, 1886-1901.

17.Peng Z and Kurgan L*. (2012) Comprehensive comparative assessment of in-silico predictors of disordered regions. Current Protein and Peptide Science, 13, 6-18.

18.Peng Z and Kurgan L*. (2012) On the complementarity of the consensus-based disorder prediction. Pacific Symposium on Biocomputing, 176-187.

19.Howell M, Green R, Killeen A, Wedderburn L, Picascio V, Alejandro A, Peng Z, Larina M, Xue B, Kurgan L and Uversky VN*. (2012) Not that Rigid Midgets and not so Flexible Giants: On the Abundance and Roles of Intrinsic Disorder in Short and Long Proteins. Journal of Biological System, 20, 471-511

20.Peng Z, Yang J* and Chen X. (2010) An improved classification of G-protein-coupled receptors using sequence-derived features. BMC Bioinformatics, 11, 420.

21.Yang J*, Peng Z and Chen X. (2010) Prediction of protein structural classes for low-homology sequences based on predicted secondary structure. BMC Bioinformatics, 11 Suppl 1, S9.

22.Yang J#, Peng Z#, Yu Z, Zhang R, Anh V and Wang D. (2009) Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation. Journal of Theoretical Biology, 257, 618-626.