姓名
牛晓辉
性别
男


职称
副教授
学位
博士

电话

邮箱
niuxiaoh@mail.hzau.edu.cn

工作单位
华中农业大学信息学院

研究方向
1.遗传变异功能解析：长期从事基于生物信息、生物统计、机器学习方法，整合多组学大样本生物数据，批量解析遗传变异生物学功能；
2.功能元件和转录调控事件预测：长期从事基于机器学习、深度学习的基因组顺式调控元件和转录调控事件预测，如转录因子结合位点预测、增强子预测、增强子核心区域预测、可选择性多聚腺苷酸化、可变转录起始位点预测。

教育经历
1997年9月---2001年7月就读于武汉大学数学与计算机科学学院，获学士学位；
2001年9月---2004 年7月就读于武汉大学数学与统计学院信息与计算科学专业，获硕士学位；
2008年9月----2012年6月 就读于华中科技大学公共卫生学院流行病与卫生统计专业，获博士学位。

主要职历
2004年7月-2014年7月 华中农业大学理学院 信息与数学科学系；
2006年12月 获讲师职称；
2013年12月 获副教授职称；
2014年7月-至今 华中农业大学 信息学院。

科研成果
1. 个人简介：
牛晓辉，男，副教授、硕士生导师，2001年本科毕业于武汉大学信息与计算科学专业，2004年获武汉大学计算数学硕士学位，2012年获华中科技大学流行病与卫生统计专业博士学位。2015年聘为硕士生导师。2017年5月-2018年5月在美国加州圣地亚哥分校(UCSD)做国家公派访问****，其间于2017年11月-2018年05月任加州大学圣地亚哥分校助理研究员。
研究方向为生物统计、生物信息学，多年来致力于利用统计和机器学习方法解决生物信息、生物医学领域的重要问题，如DNA调控元件预测、蛋白质结构和功能预测等方面的应用研究。目前，重点展开基于整合基因组、转录组、代谢组等多组学数据解析遗传变异对人类复杂疾病，尤其是癌症的影响和作用机制，以及将纵向数据的统计分析方法用于医学与生物数据的分析 。以第一作者、通讯作者或共同通讯作者在Briefings in Bioinformatics、Nucleic Acids Research、Frontiers in Genetics等杂志上发表SCI论文近20篇。
2. 科研项目：
[1] 主持国家青年基金《基于支撑向量的生存分析方法的研究与应用》；
[2] 主持武汉市卫计委医学科研项目《大病保险在新农合医疗体系中的运行分析和对策研究》；
[3] 作为第一参与人，参与国家青年基金《基于生物信息学和自然语言处理的水稻抗病基因挖掘》；
[4]作为主要参与者，参与国家自然科学基金面上项目《多种癌症中遗传变异对可选择性多聚腺苷酸化影响的深度挖掘及功能研究》；
[5] 作为主要 参与者，参与国家面上基金项目《青少年新型毒品滥用预防的亲子“认知-技能-心理成长”干预模式研究》；
[6] 作为主要参与人，参与国家青年基金《半监督排序的局部学习算法设计与推广性能研究》；
3. 科研论文：
[1]Yingjie Gao#,Zhiquan Yang#,Wenqian Yang#,Yanbo Yang,Jing Gong*,Qing-Yong Yang*,Xiaohui Niu*. Plant-ImputeDB: an integrated multiple plant reference panel database for genotype imputation.Nucleic Acids Research, Volume 49, Issue D1, 8 January 2021, Pages D1480–D1488.
[2]Hao Chen#,Yaoyao He#,Cecheng Zhao#,Lili Zheng,Ning Pan,Jianfeng Qiu,Zhaoxi Zhang*,Xiaohui Niu*,Zilong Yuan*. Reproducibility of radiomics features derived from intravoxel incoherent motion diffusion-weighted MRI of cervical cancer. Acta Radiologica,2020,https://doi.org/10.1177/0284**1.
[3]Zhifeng Huang,Yu Gan,Kun Yang,Liangdi Gao,Bing Xiong,Hanhua Li,Xiaohui Niu,Kejian Wang*,Wen Lai*. Characteristics and Evolution of Microbial Drug Resistance in Burnt Patients.Journal of Burn Care & Research,2020,iraa039,https://doi.org/10.1093/jbcr/iraa039.
[4]Yibeltal Arega, Hao Jiang,Shuangqi Wang, Jingwen Zhang,Xiaohui Niu,Guoliang Li*. ChIAMM: A Mixture Model for Statistical Analysis of Long-Range Chromatin Interactions From ChIA-PET Experiments.Frontiers in Genetics, 2020,https://doi.org/10.3389/fgene.2020.616160.
[5]Huikuan Chu,Tao Bai,Liuying Chen,Lilin Hu,Li Xiao,Lin Yao,Rui Zhu,Xiaohui Niu,Zhonglin Li,Lei Zhang,Chaoqun Han,Shuangning Song,Qi He,Ying Zhao,Qingjing Zhu,Hua Chen,Bernd Schnabl,Ling Yang,Xiaohua Hou. Multicenter Analysis of Liver Injury Patterns and Mortality in COVID-19. Frontiers in Medicine,2020,20;7:584342.
[6]Weiwei Jin,Qizhao Zhu,Yanbo Yang,Wenqian Yang,Dongyang Wang,Jiajun Yang,Xiaohui Niu,Debing Yu,Jing Gong.Animal-APAdb: a comprehensive animal alternative polyadenylation database.Nucleic Acids Research, Volume 49, Issue D1, 8 January 2021, Pages D47–D54.
[7] 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.
[8]Xiaohui Niu, Kaixuan Deng, Lifen Liu, Kun Yang, Xuehai Hu*, A statistical framework for predicting critical regions of p53-dependent enhancers, Briefings in Bioinformatics, , bbaa053,https://doi.org/10.1093/bib/bbaa053
[9] Sabrina Richardson, Tuo Lin, Yangyi Li,Xiaohui Niu, Manfei Xu Valerie Stander, Xin M Tu* . Guidance for use of weights: an analysis of different types of weights and their implications when using SAS PROCs.General Psychiatry,2019;32:e100038. doi:10.1136/ gpsych-2018-100038
[10]Xiaohui Niu#, Kun Yang#, Ge zhang, Zhiquan Yang, Xuehai Hu*. A pretraining-Retraining strategy of deep learning imporoves cell-specific enhancer predictions. Frontier in Genetics,doi: 0.3389/fgene.2019.01305
[11] Qiujian Chen,Xiaohui Niu,Nana Li. Exploring the natural chemiome to target interleukin-6 receptor (IL-6R) cytokines: an atomic scale investigation for novel rheumatoid arthritis drug discovery.Braz. J. Pharm. Sci. 2017;53(3):e17256.
[12] Duo Chen, Ashfaq Ali, Xiaohui Yong, Changgen Lin,Xiaohui Niu, Aiming Cai, Bicheng Dong, Zhixiang Zhou, Yongjian Wang*, Feihai Yu. A multi-species comparison of selective placement patterns of ramets in invasive alien and native clonal plants to light, soil nutrient and water heterogeneity. Science of the Total Environment,2019,657: 1568-1577.
[13]Niu Xiaohui, Hu Xuehai. Improved Prediction of DNA-Binding Proteins Using Chaos Game Representation and Random Forest. Current Bioinformatics, 2016,11(2):156-163.
[13] Changge Guan,Xiaohui Niu, Feng Shi, Kun Yang, Nana Li*.Predicting a DNA-binding protein using random forest with multiple mathematical features. Bio-Medical Materials and Engineering 2015, 26, S1,883–889.
[14]Niu Xiaohui, Hu Xuehai*, Shi Feng, Xia Jingbo. Predicting DNA binding proteins using support vector machine with hybrid fractal features.J Theor Biol.，2014,334:189-192.
[15]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.
[16]NiuXiaohui, LiNana,XiaJingbo,ChenDingyan,PengYuehua,XiaoYang, Weiwei quan,WangDongming,WangZengzhen. Using the concept of Chou's pseudo amino acid composition to predict protein solubility: An approach with entropies in information theory.J Theor Biol., 2013,332:211-217.
[17]Niu Xiaohui, Li Nana, Chen Dinyan, Wang Zengzhen. Interconnectionbetween the Protein Solubility and Amino Acid and Dipeptide Compositions[J]. Protein and Peptide Letters. Protein and Peptide Letters 2013，20(1):88-95.
[18]Niu Xiaohui, Hu Xuehai *, Shi Feng, Xia Jingbo. Predicting Protein Solubility by the General Form of Chou’s Pseudo Amino Acid Composition: Approached from Chaos Game Representation and Fractal Dimension[J]. Protein and Peptide Letters 2012，19(9):940-948.
[19] XiaJingbo,ZhangSilan,ShiFeng,XiongHuijuan,HuXuehai,NiuXiaohui,LiZhi. Using the concept of pseudo amino acid composition to predict resistance gene against Xanthomonasoryzaepv.oryzae in rice: An approach from chaos games representation[J]，Journal of Theoretical Biology 2011 284 16-23.
[20]Niu Xiaohui,Li Nana, Shi Feng ,Hu Xuehai, Xia Jinbo. Predicting Protein Solubility with a hybrid method by Support Vector Machine and BP Neural Network[J]. PROTEIN AND PEPTIDE LETTERS, 2010 17 (12): 1466-1472.
[21] Xia Jinbo,Hu Xuehai,Shifeng,Niu Xiaohui,Zhang ChengJun*， SVM method on predicting resistance gene against Xanthomonasoryzaepv. oryzae in rice[J].EXPERT SYSTEMS WITH APPLICATIONS, 2010 37 (8):5946-5950.
[22]Niu Xiaohui,Li Nana*,Shi Feng, Hu Xuehai. Subcellular Locations Prediction of Proteins Based on Chaos Game Representation[C]//ICBBE, 2009.(EI)
[23] Hu Xuehai, Song Chaohong, Xia Jinbo,Niu Xiaohui, Ma Xuan, Shi Feng.Chaos game representation for discriminating Thermophilic from Mesophilic protein sequences[C]//ICBBE 2009.
[24] Li Nana,NiuXiaohui*, Shi Feng, Xia Jinbo.A Novel Method to Reconstruct Phylogeny Tree based on the Chaos Game Representation[J]. J. Biomedical Science and Engineering, 2009, 1, 59-63.
[25] Niu Xiaohui, Li Nana*, Shi Feng, Li Xueyan. A Novel Measurement of Sequence Dissimilarity and Its Application to Phylogeny.[C]//theFourth International Conference on Natural Computation ,ICNC 2008.

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