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研究生导师

张永清



张永清,工学博士,副教授;

Email: zhangyq@cuit.edu.cn

科研和研究生招生方向:

1、人工智能与数据挖掘

2、机器学习与生物信息学

个人简介:

由四川大学和美国加州大学圣地亚哥分校(UCSD, University of California, San Diego)博士联合培养,长期从事机器学习、数据挖掘、生物信息学、脑机接口方面的应用研究。主持和参与包括国家自然科学基金、军委科技委、中国博士后基金在内的科研项目8项。目前在国内外具有影响力期刊和国际会议上发表SCI、EI论文40余篇,申请发明专利5项。高质量期刊包括:Nucleic Acids Research, Briefings in Bioinformatics, Expert Systems with Applications, BMC Genomics 、BMC Bioinformatics, Engineering Applications of Artificial Intelligence, International Journal of Machine Learning and Cybernetics、Chemometrics and Intelligent Laboratory、自动化学报、电子科技大学学报等。目前担任中国计算机学会生物信息专业委员会委员、计算机应用专业委员会委员、YOCSEF成都分论坛副主席、中国自动化学会智能健康与生物信息专业委员会委员、中国人工智能学会生物信息学与人工生命专业委员会委员、四川省生物信息学会理事会委员,国家基金委项目评审专家,TCBB、NCAA、Briefings in Bioinformatics、IEEE J BIOMED HEALTH、IEEE ACCESS、J BIOINF COMPUT BIOL等期刊审稿人。

承担科研项目:

1. 国家自然科学基金青年科学基金项目,高通量数据和深度学习在基因调控层次网络构建中的应用研究(61702058),课题负责人。

2. 中国博士后科学基金面上基金,基于膜电压驱动的Spiking神经网络学习算法研究(2017M612948),课题负责人。

3. 成都信息工程大学中青年学术带头人科研基金,基于智能计算的基因网络研究(J201706),课题负责人。

4. 军委科技委创新特区子课题项目,情绪反馈调节系统(2018Z007),主研。

5. 军委科技委创新特区子课题项目,脑波音乐实时生成系统(2018Z130),主研。

目前授课:

“计算机组成原理”,本科生

“人工智能”,本科/研究生

近5年代表性论文(*为通讯作者):

1. Yongqing Zhang, Zixuan Wang, Yuanqi Zeng, Yuhang Liu, Shuwen Xiong, Maocheng Wang, Jiliu Zhou, Quan Zou*, A novel convolution attention model for predicting transcription factor binding sites by combination of sequence and shape, Briefings in Bioinformatics, Volume 23, Issue 1, 2022, bbab525. (中科院二区,IF=11.6)

2. Yongqing Zhang, Zixuan Wang, Yuhang Liu, and Quan Zou*, By hybrid neural networks for prediction and interpretation of transcription factor binding sites based on multi-omics, 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2021, pp. 594-599. (CCF B类会议)

3. Yongqing Zhang, Qingyuan Chen, Meiqin Gong, Yuanqi Zeng, Dongrui Gao*, Gene regulatory networks analysis of muscle-invasive bladder cancer subtypes using differential graphical model. BMC Genomics 22, 863 (2021). (中科院二区,IF=4.0)

4. Zixuan Wang, Xiaoyao Tan, Beichen Li, Yuhang Liu, Qi Shao, Zijing Li, Yihan Yang, Yongqing Zhang*, BindTransNet: A Transferable Transformer-Based Architecture for Cross-Cell Type DNA-Protein Binding Sites Prediction. In: Wei Y., Li M., Skums P., Cai Z. (eds) Bioinformatics Research and Applications. ISBRA 2021.(CCF C类会议)

5. Yongqing Zhang, Siyu Chen, Wenpeng Cao, Peng Guo, Dongrui Gao, Manqing Wang, Jiliu Zhou, Ting Wang*, MFFNet: Multi-dimensional Feature Fusion Network based on attention mechanism for sEMG analysis to detect muscle fatigue, Expert Systems with Applications, Volume 185, 2021, 115639. (中科院一区,IF=7.0)

6. Yongqing Zhang, Zixuan Wang, Yuanqi Zeng, Jiliu Zhou, Quan Zou*, High-resolution transcription factor binding sites prediction improved performance and interpretability by deep learning method, Briefings in Bioinformatics, Volume 22, Issue 6, 2021, bbab273 (中科院二区,IF=11.6)

7. Yongqing Zhang, Shaojie Qiao*, Yuanqi Zeng, Dongrui Gao, Nan Han, Jiliu Zhou, CAE-CNN: Predicting transcription factor binding site with convolutional autoencoder and convolutional neural network, Expert Systems with Applications, Volume 183,2021,115404.(中科院一区,IF=7.0)

8. Jiaxin Xie, Siyu Chen, Yongqing Zhang, Dongrui Gao and Tiejun Liu*, Combining generative adversarial networks and multi-output CNN for motor imagery classification, Journal of Neural Engineering, Volume 18, Number 4,2021. (中科院二区,IF=5.4)

9. 郜东瑞,周晖,林志宇,冯李逍,张云霞,彭茂琴,张永清*,基于特征融合和粒子群优化算法的运动想象脑电识别方法,电子科技大学学报,2021,50(3):467-475. (EI期刊)

10. Yongqing Zhang, Qingyuan Chen, Dongrui Gao, and Quan Zou*, GRRFNet: Guided Regularized Random Forest-based Gene Regulatory Network Inference Using Data Integration, IEEE BIBM 2020, 132-139.(CCF B类会议)

11. 张永清、卢荣钊、乔少杰*,周激流,一种基于样本空间的类别不平衡数据采样方法,自动化学报,2020(46)11. (CCF T1类期刊)

12. Yongqing Zhang, Yanjian Rong, Siyu Chen, Meiqin Gong, Dongrui Gao, Min Zhu*, Wei Gan, A Review on the Application of Deep Learning in Bioinformatics, Current Bioinformatics, 2020:15 (8), 898-911.(中科院四区,IF=3.5)

13. Yongqing Zhang, Shaojie Qiao*, Rongzhao Lu, Nan Han, Dingxiang Liu, Jiliu Zhou, “How to balance the bioinformatics data: pseudo-negative sampling”, BMC Bioinformatics. 20(S25).(中科院三区,IF=3.2)

14. Yongqing Zhang, Shaojie Qiao*, Shengjie Ji, Yizhou Li, “DeepSite: Bidirectional LSTM and CNN Models for Predicting DNA-protein Binding”, International Journal of Machine Learning and Cybernetics. 11, 841-851(2020). (中科院三区,IF=4.0)

15. Yuanqi Zeng, Meiqin Gong, Meng Lin, Dongrui Gao, Yongqing Zhang*, A Review about Transcription Factor Binding Sites Prediction Based on Deep Learning, IEEE ACCESS, 2020(8): 219256-219274. (中科院三区,IF=3.6)

16. Yongqing Zhang, Shaojie Qiao*, Shengjie Ji, Nan Han, Dingxiang Liu, Jiliu Zhou, “Identification of DNA-Protein Binding Sites by Bootstrap Multiple Convolutional Neural Networks”, Engineering Applications of Artificial Intelligence, Volume 79, March 2019, Pages 58-66.(中科院二区,IF=6.2)

17. Zhang, Yongqing; Cao, Xiaoyi; Zhong, Sheng*, “GeNemo: a search engine for web-based functional genomic data”, Nucleic Acids Research, 20 April, 2016,44,W122-127.(中科院一区,IF=17.0)

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