· My main interests lie in two aspects: Integration analysis and mining of biological big data for diseases and longitudinal data analysis. 1. In biological big data analysis, our group aims to find the hidden knowledge behind biological big data. Our group integrates high-throughput and multi-omic biological data from some public databases, such as TCGA, ENCODE and GTEx, and develops some computational algorithms with machine learning and statistical methods to solve some biological problems of diseases, especially pan-cancers. 2. In longitudinal data analysis, our group aims to construct statistical models and perform statistical inference with Generalized Estimation Equations to analyse longitudinal data. |
05/2017-05/2018 |
Visiting Scholar, Division of Biostatistics and Bioinformatics, University of California San Diego, California, USA |
07/2014-present |
Associate Professor in Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, China |
01/2014-06/2014 |
Associate Professor in Mathematics and Bioinformatics, College of Science, Huazhong Agricultural University, Wuhan, China |
01/2007-12/2013 |
Lecturer in Mathematics, College of Science, Huazhong Agricultural University, Wuhan, China |
09/2008-07/2012 |
PhD in Epidemiology and Health Statistics, Huazhong University of Science and Technology, Wuhan, China |
07/2004-12/2006 |
Assistant Professor in Mathematics, College of Science, Huazhong Agricultural University, Wuhan, China |
09/2001-07/2004 |
Master in Mathematics, Wuhan University, Wuhan, China |
09/1997-07/2001 |
Bachelor in Mathematics, Wuhan University, Wuhan, China |
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[1] Xiaohui Niu#, Kaixuan Deng, Lifen Liu, Kun Yang, Xuehai Hu*. A statistical framework for predicting critical regions of p53-dependent enhancers. Briefings in Bioinformatics, doi: https://doi.org/10.1093/bib/bbaa053. (SCI, IF: 9.101) [2] Wenqian Yang#, Yanbo Yang#, Cecheng Zhao, Kun Yang, Dongyang Wang, Jiajun Yang, Xiaohui Niu*, Jing Gong*.Animal-ImputeDB: a comprehensive database with multiple animal reference panels for genotype imputation. Nucleic Acids Research, gkz854, https://doi.org/10.1093/nar/gkz854. (SCI，IF：11.147) [3] Xiaohui Niu#, Kun Yang#, Ge Zhang, Zhiquan Yang and Xuehai Hu*. A Pretraining-Retraining Strategy of Deep Learning Improves Cell-Specific Enhancer Predictions. Frontiers in Genetics, 2020 Jan 8;10:1305. doi: 10.3389/fgene.2019.01305. (SCI，IF：3.517) [4] Xiaohui Niu#, Xuehai Hu*. Improved Prediction of DNA-Binding Proteins Using Chaos Game Representation and Random Forest. Current Bioinformatics, 2016,11(2):156-163. (SCI, IF：1.189) [5]Xiaohui Niu#, Xuehai Hu*, Feng Shi and Jingbo Xia. Predicting DNA binding proteins using support vector machine with hybrid fractal features. Journal of Theoretical Biology, 2014, 343, 186-192. (SCI，IF：2.496) [6] Niu Xiaohui#, Shi Feng, Hu Xuehai, Xia Jingbo, Li Nana*. Predicting the protein solubility by integrating chaos games representation and entropy in information theory. Expert Systems with Applications, 2014, 41(4):1672-1679.(SCI, IF：2.981) [7] Xiaohui Niu#, Xuehai Hu*, Feng Shi and Jingbo Xia. Predicting DNA binding proteins using support vector machine with hybrid fractal features. Journal of Theoretical Biology, 2014, 343, 186-192. (SCI，IF：2.496) [8] Niu Xiaohui#, Li Nana,Xia Jingbo,Chen Dingyan,Peng Yuehua,Xiao Yang , Wei Weiquan,Wang Dongming,Wang Zengzhen*. Using the concept of Chou's pseudo amino acid composition to predict protein solubility: An approach with entropies in information theory. Journal of Theoretical Biology, 2013,332:211-217. (SCI，IF：2.303) |
