教师基本信息
职称:讲师
职务:青年副研究员
电子邮箱:wangyi_fudan@fudan.edu.cn
办公地点:生科大楼B603
办公电话:13764841280
个人网页/课题组主页:www.drwang.top
个人简介
2000.9-2004.7: 复旦大学生命科学学院,学士
2004.9-2009.12:复旦大学现代人类学教育部重点实验室,遗传学博士
2010.4-2011.8: 美国贝勒医学院人类基因组测序中心,博士后
2012.8-2016.7: 复旦大学现代人类学教育部重点实验室,助理研究院
2016.8-今: 复旦大学生命科学学院,青年副研究员
研究方向
医学遗传学、生物信息、医学人工智能
授课情况
《生命科学中的机器学习》
招生专业
生命科学、计算机科学、医学
代表性论文和论著
Xiaojian Liu, Yuanyuan Yang, Yan Qiu, Md. Reyad-ul-ferdous, Qiurong Ding*, Yi Wang* (2020) SeqCor: correct the effect of gRNA sequences in CRISPR/Cas9 screenings by machine learning algorithm. Journal of Genetics and Genomics. (即将出版)
Yi Li, Meng Liang, Xianhong Yin, Xiaoyu Liu, Meng Hao, Zixin Hu, Yi Wang*, Li Jin* (2020) COVID-19 epidemic outside China: 34 founders and exponential growth. J Investig Med. 2020 Oct 6;jim-2020-001491. doi: 10.1136/jim-2020-001491
Wang Y#, Li Y#, Hao M#, Liu X#, Zhang M, Wang J, Xiong M, Shugart YY, Jin L. (2019) Robust Reference Powered Association Test of Genome-Wide Association Studies. Front Genet. 2019 Apr 9;10:319. doi: 10.3389/fgene.2019.00319. eCollection 2019.
Wang Y#, Li Y#, Qiao C#, Liu X#, Hao M#, Shugart YY, Xiong M, Jin L. (2018) Nuclear Norm Clustering: a promising alternative method for clustering tasks. Sci Rep. 2018 Jul 18;8(1):10873.
Sun R#, Wang Y#, Jin M#, Chen L, Cao Y, Chen F. (2018) Identification and Functional Studies of MYO1H for Mandibular Prognathism. J Dent Res. 2018 Jul 1:22034518784936.
Li Z#, Wang Y#, Wang F, (2018) A study on fast calling variants from next-generation sequencing data using decision tree. BMC Bioinformatics. 2018 Apr 19;19(1):145
Zhou W#, Wang Y#, Fujino M, Shi L, Jin L, Li XK, Wang J.(2018) A standardized fold change method for microarray differential expression analysis used to reveal genes involved in acute rejection in murine allograft models. FEBS Open Bio. 2018 Jan 25;8(3):481-490
Wang Y#, Li Y#, Liu X#, Pu W, Wang X, Wang J, Xiong M, Yao Shugart Y, Jin L.(2017) Bagging Nearest-Neighbor Prediction independence Test: an efficient method for nonlinear dependence of two continuous variables. Sci Rep 2017 Oct 06;7(1).
Pan X#, Wang Y#, Wong EHM, Telenti A, Venter JC, Jin L.(2017).Fine population structure analysis method for genomes of many. Sci Rep 2017 Oct 03;7(1).
Li L#, Wang Y#, Yang S, Xia M, Yang Y, Wang J, Lu D, Pan X, Ma T, Jiang P, Yu G, Zhao Z, Ping Y, Zhou H, Zhao X, Sun H, Liu B, Jia D, Li C, Hu R, Lu H, Liu X, Chen W, Mi Q, Xue F, Su Y, Jin L, Li S.(2017). Genome-wide screening for highly discriminative SNPs for personal identification and their assessment in world populations. Forensic Sci Int Genet. 2017 May;28:118-127.
Chen Y#, Zhao L#, Wang Y#, Cao M, Gelowani V, Xu M, Agrawal SA, Li Y, Daiger SP, Gibbs R, Wang F, Chen R(2017). SeqCNV: a novel method for identification of copy number variations in targeted next-generation sequencing data. BMC Bioinformatics. 2017 Mar 3;18(1):147.
Liu S#, Wang Y#, Wang F(2016). A fast read alignment method based on seed-and-vote for next generation sequencing. BMC Bioinformatics. 2016 Dec 23;17(Suppl 17):466.
Wang Y#, Li Y#, Pu W, Wen K, Shugart YY, Xiong M, Jin L (2016). Random Bits Forest: a Strong Classifier/Regressor for Big Data. Sci Rep. 2016 Jul 22;6:30086.
Yi Wang#, Yi Li#, Momiao Xiong, Yin Yao Shugart, Li Jin (2016), Random Bits Regression: a Strong General Predictor for Big Data. Big Data Analytics 20161:12
Wang, Y#., Y. Li#, H. Cao, M. Xiong, Y. Y. Shugart and L. Jin (2015). "Efficient test for nonlinear dependence of two continuous variables." BMC Bioinformatics 16(1): 260.
Wang Y#, Lu J#, Yu J, Gibbs RA, Yu F (2013) An integrative variant analysis pipeline for accurate genotype/haplotype inference in population NGS data. Genome Res 23:833-842
Abecasis, G. R., A. Auton, L. D. Brooks, M. A. DePristo, R. M. Durbin, R. E. Handsaker, H. M. Kang, G. T. Marth and G. A. McVean (2012). "An integrated map of genetic variation from 1,092 human genomes." Nature 491(7422): 56-65. (5.2 Low coverage SNP calling: Baylor College of Medicine HGSC, section一作)
Ling ZQ#, Wang Y#, Mukaisho K, Hattori T, Tatsuta T, Ge MH, Jin L, Mao WM, Sugihara H (2010) Novel statistical framework to identify differentially expressed genes allowing transcriptomic background differences. Bioinformatics 26:1431-1436