我是实验室个人介绍2
Quanzhong_Liu
  • 姓名:刘全中  性别:男  职称:副教授  
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  • 通讯地址:陕西省杨凌农业高新技术产业示范区 邮编:712100
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  • 电子邮箱: liuqzhong@nwsuaf.edu.cn
工作学习经历

1999.09-2003.07  河南大学 计算机与信息工程学院 计算机科学与技术专业 学士
2003.09-2006.06  西北工业大学 计算机学院 计算机软件与理论 硕士
2007.09-2012.10  西北农林科技大学 机械与电子工程学院 农业电气化与自动化 博士
2004.01-2006.01  西安软件园 NECAS有限公司 软件工程师
2006.06-2008.10  西北农林科技大学 信息工程学院 计算机科学系 助教
2009.02-2010.02  美国马萨诸塞大学 电子工程系 访问学习
2008.10-2014.12  西北农林科技大学 信息工程学院 软件工程系 讲师
2015.01-至今  西北农林科技大学 信息工程学院 软件工程系 副教授

研究方向

数据挖掘、计算生物学

讲授课程

目前主要讲授《算法设计与分析》、《Java 语言程序设计》、《Java EE》、《大数据管理》。

参与科研项目

参与了国家自然基金、国家重点研发、国家科技支撑计划等多个项目,研发了多套软件系统。

论文专著

15. Zhang, X; Zhao, LW ; Chai, ZY; Wu, H ; Yang, W ; Li, C; Jiang, Y *;Liu, QZ*, (2024). NPI-DCGNN: An Accurate Tool for Identifying ncRNA-Protein Interactions Using a Dual-Channel Graph Neural Network.. JOURNAL OF COMPUTATIONAL BIOLOGY .DOI: 10.1089/cmb.2023.0449

14. Zhao, LW; Hao, R; Chai, ZY; Fu, WW ; Yang, W; Li, C; Liu, QZ* Jiang, Y*., (2024). DeepOCR: A multi-species deep-learning framework for accurate identification of open chromatin regions in livestock.Computational Biology and Chemistry. 110

13. Wang, X ; Chai, ZY ; Li, SH ; Liu, Y ; Li, C*; Jiang, Y*, Liu, QZ*(2024) CTISL: a dynamic stacking multi-class classification approach for identifying cell types from single-cell RNA-seq data. Bioinformatics, 40(2)

12. Chen, JX ; Wang, M; Zhao, DF; Li, FY ; Wu, H; Liu, QZ ; Li, SQ* ,(2023) MSINGB: A Novel Computational Method Based on NGBoost for Identifying Microsatellite Instability Status from Tumor Mutation Annotation Data. Interdisciplinary Sciences: Computational Life Sciences .15(1),100-110

11. Liu Q,Fang H, Wang M, Li S, Coin LJM, Li F*, Song J*.(2022). DeepGenGrep: a general deep learning-based predictor for multiple genomic signals and regions. Bioinformatics. 2022, 38(17):4053–4061. 

10. Wang, M., Li, F., Wu, H., Liu Q., & Li, S. (2022). PredPromoter-MF(2L): A Novel Approach of Promoter Prediction Based on Multi-source Feature Fusion and Deep Forest. Interdisciplinary Sciences: Computational Life Sciences. doi:10.1007/s12539-022-00520-4

9. Liu Q., Chen, J., Wang, Y., Li, S., Jia, C., Song, J., & Li, F. (2021). DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites. Briefings in Bioinformatics, 22(3), bbaa124. doi:10.1093/bib/bbaa124

8. Liang, X., Li, F., Chen, J., Li, J., Wu, H., Li, S., . . . Liu Q. (2021). Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification. Briefings in Bioinformatics, 22(4), bbaa312. doi:10.1093/bib/bbaa312

7. Chen, J., Li, F., Wang, M., Li, J., Marquez-Lago, T. T., Leier, A., . . . Song, J. (2021). BigFiRSt: A Software Program Using Big Data Technique for Mining Simple Sequence Repeats From Large-Scale Sequencing Data. Front Big Data, 4, 727216. doi:10.3389/fdata.2021.727216

6. Li, F., Leier, A., Liu Q., Wang, Y., Xiang, D., Akutsu, T., . . . Song, J. (2020). Procleave: Predicting Protease-specific Substrate Cleavage Sites by Combining Sequence and Structural Information. Genomics, Proteomics & Bioinformatics, 18(1), 52-64. doi:https://doi.org/10.1016/j.gpb.2019.08.002

5. Li, F., Fan, C., Marquez-Lago, T. T., Leier, A., Revote, J., Jia, C., . . . Song, J. (2020). PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact. Briefings in Bioinformatics, 21(3), 1069-1079. doi:10.1093/bib/bbz050

4. Li, F., Chen, J., Leier, A., Marquez-Lago, T., Liu Q., Wang, Y., . . . Song, J. (2019). DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites. Bioinformatics, 36(4), 1057-1065. doi:10.1093/bioinformatics/btz721 %J Bioinformatics

3. Liu Q., Song, J., & Li, J. (2016). Using contrast patterns between true complexes and random subgraphs in PPI networks to predict unknown protein complexes. Scientific Reports, 6(1), 21223. doi:10.1038/srep21223

2. Liu Q., Shi, P., Hu, Z., & Zhang, Y. (2014). A novel approach of mining strong jumping emerging patterns based on BSC-tree. International Journal of Systems Science, 45(3), 598-615. doi:10.1080/00207721.2012.724110

1. Liu Q., Chen, C., Zhang, Y., & Hu, Z. (2011). Feature selection for support vector machines with RBF kernel. Artificial Intelligence Review, 36(2), 99-115. doi:10.1007/s10462-011-9205-2