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刘全中

发布日期：2022-07-14　 　阅读次数：

基本信息

刘全中，河南平舆人，副教授，硕士生导师，工学博士。

研究方向

数据挖掘、机器学习、计算生物学

开设课程

本科生课程：《算法设计与分析》、《C语言程序设计》、《Java EE》

研究生课程：《大数据管理》

学习工作经历

1999.09-2003.07  河南大学 计算机科学与技术专业 学士

2003.09-2006.06 西北工业大学 计算机软件与理论  硕士

2007.09-2012.10 西北农林科技大学 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 访问学者

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

科学研究

工作至今，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，主持研发了多套实用型软件系统。在Artificial Intelligence Review、Frontiers in Big Data、Scientific Reports、Briefings in Bioinformatics、Bioinformatics等期刊上发表学术论文10余篇。


[1] Liu Q, Fang H, Wang M, Li S, Coin LJM, Li F*, Song J*. “DeepGenGrep: a general deep learning-based predictor for multiple genomic signals and regions”. Bioinformatics. 2022, DOI:10.1093/bioinformatics/btac454

[2] Chen Z, Liang B, Wu Y, Liu Q, Zhang H, Wu H. Integrating multi-omics data to identify dysregulated modules in endometrial cancer. Briefings in Functional Genomics. 2022, DOI: 10.1093/bfgp/elac010

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

[4] Chen J, Li F, Wang M, Li J, Marquez-Lago TT, Leier A, Revote J, Li S, Liu Q*, Song J*. “BigFiRSt: a software program using big data technique for mining simple sequence repeats from large-scale sequencing data”. Frontiers in Big Data. 2022, 4, 727216.

[5] Wu H, Wu Y, Jiang Y, Zhou B, Chen Z, Xiong Y, Liu Q, Zhang H. scHiCStackL: a stacking ensemble learning-based method for single-cell Hi-C classification using cell embedding[J]. Briefings in Bioinformatics. 2021, 23(5590).

[6] Wu H, Chen Z, Wu Y, Zhang H, & Liu Q. Integrating protein–protein interaction networks and somatic mutation data to detect driver modules in pan-cancer. Interdisciplinary Sciences: Computational Life Sciences. 2021, 14(721–733).

[7] Liang X, Li F, Chen J, Li J, Wu H, Li S*, Song J*, Liu Q*. “Large-scale comparative review and assessment of computational methods for anti-cancer peptide identification”. Briefings in Bioinformatics. 2021, 22(4), bbaa312.

[8] Liu Q, Chen J, Wang Y, Li S, Jia C, Song J, Li F. “DeepTorrent: a deep learning-based approach for predicting DNA N4-methylcytosine sites”. Briefings in Bioinformatics. 2021, 22(3), bbaa124. (Clarivate高被引论文).

[9] Li F, Leier A, Liu Q, Wang Y, Xiang D, Akutsu T, Webb GI, Smith AI, Marquez-Lago TT, Li J, Song J. “Procleave: Predicting Protease-Specific Substrate Cleavage Sites by Combining Sequence and Structural Information”. Genomics, Proteomics & Bioinformatics. 2020, 18(1), 52-64.

[10] Li F, Chen J, Leier A, Marquez-Lago TT, Liu Q, Wang Y, Revote J, Smith AI, Akutsu T, Webb GI, Kurgan L, Song J. “DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substrates and cleavage sites”. Bioinformatics. 2020, 36(4), 1057-1065.

[11] Li F, Fan C, Marquez-Lago TT, Leier A, Revote J, Jia C, Zhu Y, Smith AI, Webb GI, Liu Q*, Wei L*, Li J, Song J*. “PRISMOID: a comprehensive 3D structure database for post-translational modifications and mutations with functional impact”. Briefings in Bioinformatics. 2020, 21(3), 1069-1079.

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

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

[14] Liu Q, Shi P, Hu Z. Fast Algorithms for Mining Strong Jumping Emerging Patterns Using the Contrast Pattern Tree. ICIC Express Letters, Part B: Applications, 2013, 4(1), 121-128

[15]刘全中，聂艳明，宁纪锋. 高维度的数据强跳跃显露模式挖掘方法研究. 华中科技大学学报（自然科学版）. 2013, 41(8), 55-60.

[16] Liu Q, Chen, C, Zhang Y, & Hu, Z. Feature selection for support vector machines with RBF kernel. Artificial Intelligence Review. 2011, 36(2), 99-115.

[17] Liu Q, Zhang Y, Hu Z. Extracting Decision Rules from Sigmoid Kernel. Advanced Data Mining and Applications. 2008, 5139, 294-304.