何飞
东北师范大学
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职　　称：讲师
研究方向：机器学习、深度学习、计算生物学
办公电话：**
办公地点：计算机楼二楼
电子邮件：hef740@nenu.edu.cn



个人简历
2003/09-2007/06，吉林大学，计算机科学与技术，学士 2007/09-2010/06，吉林大学，计算机科学与技术（生物信息学），硕士 2010/09-2015/06，吉林大学，计算机科学与技术（生物信息学），博士 2015/08-，东北师范大学，信息科学与技术学院，讲师2018/11-2020/11，美国密苏里大学，生命科学中心，博士后电子邮件：hef740@nenu.edu.cn主持科研项目：1. 国家自然科学基金青年项目，**，融合多视角3D深度描述子的靶蛋白-配体复合物活性预测研究，26万元，主持2. 吉林省科技厅优秀青年人才基金项目，JH，具有自适应自学习特性的纹理Gabor深度学习网络研究，8万元，主持3. 吉林省教育厅“十三五”科学技术项目，JJKH**KJ，面向动态蛋白质交互组网络的复合深度学习模型研究，2.5万元，主持已发表学术论文：（1）Fei He, Rui Wang, Yanxin Gao, Duolin Wang, Yang Yu, Dong Xu, and Xiaowei Zhao. Protein Ubiquitylation and Sumoylation Site Prediction Based on Ensemble and Transfer Learning. IEEE International Conference on Bioinformatics and Biomedicine. IEEE Computer Society, 2019:117-123. (CCF推荐B类会议).（2）Pingping Sun, Yongbing Chen, Bo Liu, Yanxin Gao, Ye Han, Fei He and Jinchao Ji.DeepMRMP: A new predictor for multiple types of RNA modification sites using deep learning.Mathematical Biosciences and Engineering.2019.(SCI, 通讯作者)（3）Fei He, Duolin Wang, Yulia Innokenteva, Olha Kholod, Dmitriy Shin and Dong Xu. 2019. Extracting Molecular Entity and Their Interactions from Pathway Figures Based on Deep Learning. In Proceedings of ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB’19). Niagara Falls, NY, USA（权威会议）（4）Chang Lu, Zhe Liu, Enju Zhang, Fei He, Zhiqiang Ma, Han Wang. MPLs-Pred: Predicting Membrane Protein-Ligand Binding Sites Using Hybrid Sequence-Based Features and Ligand-Specific Models. International Journal of Molecular Sciences.2019. 20(13), 3120.(SCI, 通讯作者)（5）Jijin Ji, Yongbing Chen,Guozhong Feng, Xiaowei Zhao, Fei He. Clustering mixed numeric and categorical data with artificial bee colony strategy.2019, Journal of Intelligent & Fuzzy Systems, 1-10. (SCI, 通讯作者)（6）Liu Yang, Wang Duolin, He Fei, Wang Juexin, Joshi Trupti and Xu Dong. (2019). Phenotype prediction and genome-wide association study using deep convolutional neural network of soybean. Frontiers in Genetics, 10, 1091.(SCI)（7）Fei He, Rui Wang, Jiagen Li, Lingling Bao, Dong Xu and Xiaowei Zhao. Large-scale prediction of protein ubiquitination sites using a multimodal deep architecture, BMC Systems Biology. 2018, 12(Suppl 6):109 (SCI)（8）Xiaowei Zhao, Jiagen Li, Rui Wang, Fei He, Lin Yue, Minghao Yin. General and Species-Specific Lysine Acetylation Site Prediction Using a Bi-Modal Deep Architecture, IEEE Access. 2018, 6: 63560-63569 (SCI)（9）Ye Han, Fei He, Yongbing Chen, Yuanning Liu and Helong Yu. SiRNA silencing efficacy prediction based on a deep architecture, BMC Genomics 2018,19(Suppl 7):670 (SCI)（10）Fei He, Lingling Bao, Rui Wang, Jiagen li, Dong Xu, Xiaowei Zhao. A multimodal deep architecture for large-scale protein ubiquitylation site prediction, IEEE International Conference on Bioinformatics and Biomedicine. IEEE Computer Society, 2017:108-113（CCF推荐B类会议）（11）Ye Han, Fei He, Xian Tan, Helong Yu. Effective small interfering RNA design based on convolutional neural network, IEEE International Conference on Bioinformatics and Biomedicine. IEEE Computer Society, 2017:16-21（CCF推荐B类会议）（12）Fei He, Ye Han, Jianting Gong, Jiazhi Song, Han Wang, Yanwen Li. Predicting siRNA efficacy based on multiple selective siRNA representations and their combination at score level, Scientific Reports,2017,7: 44836（SCI）（13）Fei He，Ye Han，Han Wang，Jinchao Ji，Yuanning Liu，Zhiqiang Ma，A Deep Learning Architecture for Iris Recognition based on Optimal Gabor Filters and Deep Belief Network, Journal of Electronic Imaging, 2017, 26(2) : 023005（SCI）（14）Yuanning Liu, Fei He, Xiaodong Zhu, Ying Chen, Yan Han, Yanning Fu. Video Sequence-Based Iris Recognition Inspired by Human Cognition Manner. Journal of Bionic Engineering, 2014, 11(3): 481-489. （导师外第一作者，SCI） （15）Yuanning Liu, Fei He, Xiaodong Zhu, Zhen Liu, Ying Chen, Ye Han, Lijiao Yu. The Improved Characteristics of Bionic Gabor Representations by Combining with SIFT Key-points for iris Recognition. Journal of Bionic Engineering, 2015, 12(3) : 504-517.（导师外第一作者，SCI）（16）Fei He, Yuanning Liu, Xiaodong Zhu, Chun Huang, Ye Han, Hongxing Dong. Multiple localfeature representations and their fusion based on an SVR model for irisrecognition using optimized Gabor filters. EURASIP Journal on Advances in Signal Processing, 2014(1), 95（SCI）（17）Fei He, Yuanning Liu, Xiaodong Zhu, Chun Huang, Ye Han, Ying Chen. Score level fusion scheme based on adaptive local Gabor features for face-iris-fingerprint multimodal biometric. Journal of Electronic Imaging, 2014, 23(3): 033019（SCI）（18）Ye Han, Yuanning Liu, Hao Zhang, Fei He, Chonghe Shu, liyan Dong. Utilizing Selected Di- and Trinucleotides of siRNA to Predict RNAi Activity. Computational & Mathematical Methods in Medicine, 2017, 2017(8):**.（SCI）

社会兼职




获奖情况 （数据来源：科学技术处、社会科学处）




教学信息 （数据来源：教务处）




科研信息 （数据来源：科学技术处、社会科学处）


项目：
1. 面向动态蛋白质交互组网络的复合深度学习模型研究，2018年
2. 融合多视角3D深度描述子的靶蛋白-配体复合物活性预测研究，2018年
3. 具有自适应自学习特性的纹理Gabor深度学习网络研究，2017年
4. 大规模纹理分类的深度Gabor卷积神经网络构建，2016年
论文：
1. Large-scale prediction of protein ubiquitination sites using a multimodal deep architecture，BMC SYSTEMS BIOLOGY，2018年
2. General and Species-Specific Lysine Acetylation Site Prediction Using a Bi-Modal Deep Architecture，IEEE ACCESS，2018年
3. A Multimodal Deep Architecture for Large-Scale Protein Ubiquitylation Site Prediction，2017 IEEE International Conference on Bioinformatics and Biomedicine(BIBM)，2017年
4. Effective small interfering RNA design based on convolutional neural network，2017 IEEE International Conference on Bioinformatics and Biomedicine(BIBM)，2017年
5. Predicting siRNA efficacy based on multiple selective siRNA representations and their combination at score level，SCIENTIFIC REPORTS，2017年
6. Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network，J ELECTRON IMAGING，2017年
7. Utilizing Selected Di- and Trinucleotides of siRNA to Predict RNAi Activity，COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE，2017年
8. Face-iris multimodal biometric scheme based on feature level fusion，J ELECTRON IMAGING，2015年