代琦 性 别：男 出生年月：1979年09月 职 称：教授 所 在 学 科 组： 生物信息学 办 公 室：0571-86843746 电子邮件：daiailiu04@yahoo.com 个人主页：http://bioinfo.zstu.edu.cn/daiqi

主要学习工作经历

1. 2000.09-2004.07河南师范大学本科学习

2. 2004.09-2009.05大连理工大学博士学习

3. 2009.05-2011.02杭州电子科技大学教师

4. 2011.02-浙江理工大学教师

主要学术及社会兼职

1. Journal of Computational Biology and Bioinformatics Research编委

2. American Journal of Bioinformatics and Computational Biology编委

3.浙江省生物信息学学会理事

4.浙江省信号处理学会会员

主要研究方向

1.功能基因组序列分析

2.肿瘤蛋白质研究

3.宫颈癌HPV分型研究

4.乳腺癌早期诊断研究

获奖与荣誉

1.荣获2006届博士生专项奖学金---“纪念向坊隆”村井隆奖学金

2.浙江省“151”人才第三层次

3.浙江省高校中青年学科带头人

4.浙江理工大学“521”拔尖人才

科研教学项目

1. “与肿瘤蛋白质结构、功能有关的信息处理问题研究”国家自然科学基金，基金号：61170316，2012.1- 2015.12, 52万（负责人）

2.“面向宫颈癌HPV分型模型的生物序列比较及分类方法研究”国家自然科学基金，基金号：61001214，2011.1- 2013.12, 24万（负责人）

3. “融合临床突变与序列的多重信息研究乳腺癌BRCA1/2基因突变区域”浙江省自然科学基金，基金号：Y2100930，2011.1- 2013.12, 10万（负责人）

4. “卵巢癌化疗反应基因标志物辨识研究”浙江省自然科学基金，基金号：Z2090299，2010.1- 2012.12, 35万（主要成员，3/7）

5. “与生物序列结构、功能有关的数学方法研究”，国家自然科学基金，基金号：10871219，2009.1- 2011.12, 23万（参与成员，6/9）

6. “数学方法在计算分子生物学中的应用”，国家自然科学基金，基金号：10571019，2006.1- 2008.12, 24万（参与成员，8/9）

专著论文

1.Qi Dai*, Yan Li, Xiaoqing Liu, Yuhua Yao, Yunjie Cao, Pingan He. Comparison study on statistical features of predicted secondary structures for protein structural class prediction: From content to position. BMC Bioinformatics, 2013, 14: 152.

2.Qi Dai*, Xiaoqing Liu, Yuhua Yao, Fukun Zhao. Using Markov model to improve word normalization algorithm for biological sequence comparison. Amino Acids, 2012, DOI 10.1007/s00726-011-0906-2.

3.Qi Dai*, Xiaodong Guo, Lihua Li. Sequence comparison via polar coordinates representation and curve tree. Journal of Theoretical Biology, 2012, 292: 78-85.

4.Qi Dai*, Lihua Li, Xiaoqing Liu, Yuhua Yao, Fukun Zhao, Michael Zhang. Integrating Overlapping Structures and Background Information of Words Significantly Improves Biological Sequence Comparison. PLOS one, 2011. 6(11): e26779.

5.Qi Dai*, Wu Li, Lihua Li. Improving protein structural class prediction using novel combined sequence information and predicted secondary structural features. Journal of Computational Chemistry, 2011, 32: 3393-3398.

6.Qi Dai*, Xiaoqing Liu, Yuhua Yao, Fukun Zhao. Numerical characteristics of word frequencies and their application to dissimilarity measure for sequence comparison. Journal of Theoretical Biology, 2011, 276(1): 174-180.

7.Xiaoqing Liu,Qi Dai*, Lihua Li, Zerong He. An efficient binomial model-based measure for sequence comparison and its application. J Biomol Struct Dyn, 2011, 28(5):833-843.

8. Xiaoqing Liu,Qi Dai*, Lihua Li, Zhilong Xiu. Resistant mechanism against nelfinavir of subtype C human immunodeficiency virus type 1 proteases. Journal of Molecular Structure, 2011, 986: 30-38.

9.Qi Dai*, Xiaoqing Liu, Lihua Li, Yuhua Yao, Bin Han, Lei Zhu. Using Gaussian Model to Improve Biological Sequence Comparison. Journal of Computational Chemistry, 2010, 31: 351-361.

10.Shuyan Ding,Qi Dai, Hongmei Liu, Tianming Wang. A simple feature representation vector for phylogenetic analysis of DNA sequences, Journal of Theoretical Biology, 2010, 265(4):618-623.

11.Yuhua Yao*,Qi Dai, Ling Li, Xu-Ying Nan, Ping-An He, Yao-Zhou Zhang. Similarity/dissimilarity studies of protein sequences based on a new 2D graphical representation. Journal of Computational Chemistry, 2010, 31(5): 1045-1052.

12.Qi Dai*, Yanchun Yang, Tianming Wang. Markov model plus k-word distributions: A synergy that produces novel statistical measures for sequence comparison, Bioinformatics, 2008, doi: 10.1093/bioinformatics/btn436.

13.Qi Dai*, Tianming Wang. Comparison study on k-word statistical measures for protein: from sequence to 'sequence space'. BMC Bioinformatics, 2008, revised.

14.Qi Dai*, Tianming Wang. Use of linear regression model to compare RNA secondary structures, Journal of Theoretical Biology, 2008, 253(4):854-60

15.Qi Dai*, Tianming Wang. Use of statistical measures for analyzing RNA secondary structures, Journal of Computational Chemistry, 2008, 29: 1292-1305.

16. Yuhua Yao,Qi Dai, Xu-Ying Nan,Ping-An He, Zuo-Ming Nie, Song-Ping Zhou, Yao-Zhou Zhang. Analysis of similarity/dissimilarity of DNA sequences based on a class of 2D graphical representation , Journal of Computational Chemistry, 2008, 29: 1632-1639.

17. Yuhua Yao,Qi Dai, Chun Li, Ping-An He, Xu-YingNan, Yao-Zhou Zhang. Analysis of similarity/dissimilarity of DNA sequences based on a class of 2D graphical representation , Proteins: Structure, Function, and Bioinformatics, 2008, 10.1002/prot.22110.

18.Qi Dai*, Xiaoqing Liu, Tianming Wang. C(i,j) matrix: A better numerical characterization for graphical representations of biological sequences, Journal of Theoretical Biology, 2007, 247: 103-109.

19.Qi Dai*, Xiaoqing Liu, Tianming Wang, Vukicevic, Damir. Linear regression model of DNA sequences and its application, Journal of Computational Chemistry, 2007, 28: 1434-1445.

20.Qi Dai*, Xiaoqing Liu, Tianming Wang. Analysis of protein sequences and their secondary structures based on transition matrices. Journal of Molecular Structure-THEOCHEM, 2007, 803: 115-122.

21.Qi Dai*, Xiaoqing Liu, Tianming Wang. Numerical characterization of DNA sequences based on the k-step Markov chain transition probability . Journal of Computational Chemistry, 2006, 27: 1830-1842.

22.Qi Dai*, Xiaoqing Liu, Tianming Wang. A novel 2D graphical representation of DNA sequences and its application. Journal of Molecular Graphics & Modelling, 2006, 25: 340-344.

23.Xiaoqing Liu,Qi Dai, Zhilong Xiu, Tianming Wang, PNN-curve: A new 2D graphical representation of DNA sequences and its application. Journal of Theoretical Biology, 2006, 243: 555-561.

软件

1. PSCP-PSSE. An integrated computational software which implements sixteen statistical features of predicted secondary structures from content to position for protein structural class prediction (http://bioinfo.zstu.edu.cn/PSCP-PSSE).

2. Mplusd. An integrated computational software which implements four statistical similarity measures proposed by us to measure the (dis)similarity of biological sequences.

3. SMPS-SS. An integrated computational software which implements six statistical measures for protein comparison, where the statistical measures are based on protein sequence or protein 'sequence space'.