Name

Xuehai Hu

Title

Associate   Professor

Institute

College of Informatics, Huazhong Agricultural   University

Address

No.1   Shizishan Street, Hongshan District, Wuhan 430070, China

Email

huxuehai@mail.hzau.edu.cn,

 ORCID: http://orcid.org/0000-0001-6731-1602

Fax

86+02787288509

Research Interest

My   main interests lie in two aspects: cis-regulatory element (CRE)   prediction and genomic prediction.

1.         In CRE   prediction, our group aims to develop novel computational algorithms including   deep learning methods and sequence complexity methods, to perform various   prediction tasks concerning CRE predictions including cell\tissue specific   enhancer prediction, cross-species enhancer prediction, chromatin accessible   DNA prediction, as well as predictions of critical regions of CRE by   analyzing CRISPR-edited CRE data.

2.         In   genomic prediction, our aims to integrate multiple omics data and employ   various regression models including LASSO, random forest, deep learning, to   learn complex biological network that is useful for phenotype predictions.

Academic activities

I have served as a reviewer for Bioinformatics、Plant Biotechnology Journal、BMC Genomics、Heredity、BMC Bioinformatics、IEEE Computational Biology and Bioinformatics and for the   funding branch of the NSFC (China).

Education and research experience

Grants (Government-funded)

Representative publications（*for corresponding authors, # for first authors）

[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] Xuehai Hu#,   Weibo Xie, Chengchao Wu, Shizhong Xu*. A directed learning strategy   integrating multiple omic data improves genomic prediction. Plant   Biotechnology Journal. 2019 Oct;17(10):2011-2020. doi: 10.1111/pbi.13117..   (SCI，IF：6.840)

[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] Chengchao Wu#, Jin   Chen#, Yunxia Liu, Xuehai Hu*. Improved Prediction of Regulatory   Element Using Hybrid Abelian Complexity Features with DNA Sequences. International   Journal of Molecular Sciences, 2019, 20, 1704. (SCI, IF：4.183)

[5] Chengchao Wu, Shixin Yao, Xinghao Li, Chujia Chen and Xuehai   Hu*. Genome-Wide Prediction   of DNA Methylation Using DNA Composition and Sequence Complexity in Human.    International   Journal of Molecular Sciences, 2017, 18, 420. (SCI, IF：4.183)

[6] Hongchu   Wang#, Xuehai Hu*. Accurate prediction of nuclear receptors with   conjoint triad feature. BMC Bioinformatics, 2015, 16:402. (SCI, IF：2.576)

[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] Jinlong   Lu, Xuehai Hu* and Donggang Hu. A new hybrid fractal algorithm for   predicting thermophilic nucleotide sequences.  Journal of Theoretical   Biology, 2012, 293, 74-81. (SCI，IF：2.496)