陈震，男，1985年6月生。博士，特聘教授，博、硕士生导师。
研究领域：生物信息学。
主要研究内容：基于人工智能的分子设计育种算法开发。
所授课程：生物信息学。
E-mail：chenzhen-win2009@163.com。
 
教育与研究/工作经历：
2020/07 - 至 今     公司，特聘教授
2016/09 - 2020/06    青岛大学，讲师
2014/08 - 2016/08    北京市农林科学院，助理研究员
2009/09 - 2014.06    中国农业大学，生物信息学，硕博连读
2005/09 - 2009/06    泰山学院，生物科学，本科
 
承担项目与课题：

1. 植物基因组调控序列突变影响的计算研究及在水稻育种中的应用，国家自然科学基金面上项目，2022~2025，主持人。
2. 蛋白质丙二酰化修饰位点预测算法开发及其与代谢疾病的关联性分析，国家自然科学基金青年基金项目，2018~2020，主持人。

 
发表的部分论文（^†为共同一作，^#为通讯作者）：

1. Chen Z^†, Zhao P^†, Li C^†, Xiang DX et al. (2021) iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization. Nucleic Acids Research. 49:e60.
2. Chen YZ, Wang ZZ, Chen Z^#, and Song JN^# (2021) nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning. Brief Bioinform. 22:bbab146.
3. Chen Z^†., Zhao P^†, Li F et al. (2020) Comprehensive review and assessment of computational methods for predicting RNA post-transcriptional modification sites from RNA sequences. Brief Bioinform. 21:1676–1696.
4. Chen Z^†., Zhao P^†, Li F et al. (2020) iLearn: An integrated platform and meta-learner for feature engineering, machine learning analysis and modeling of DNA, RNA and protein sequence data. Brief Bioinform. 21:1047–1057.
5. Chen Z., He, N., Huang, Y., Qin, W.T., Liu, X. and Li, L. (2018) Integration of A Deep Learning Classifier with A Random Forest Approach for Predicting Malonylation Sites. Genomics Proteomics Bioinformatics. 16:451–9.
6. Zhang, L., Zou, Y., He, N., Chen, Y., Chen, Z^#. and Li, L. (2020) DeepKhib: A Deep-Learning Framework for Lysine 2-Hydroxyisobutyrylation Sites Prediction. Front Cell Dev Biol, 8, 580217.
7. Zhao, Y., He, N., Chen, Z^#. and Li, L. (2020) Identification of Protein Lysine Crotonylation Sites by a Deep Learning Framework With Convolutional Neural Networks. IEEE Access, 8, 14244-14252.
8. Huang, Y., He, N., Chen, Y., Chen Z^#. and Li, L^#. (2018) BERMP: a cross-species classifier for predicting m(6)A sites by integrating a deep learning algorithm and a random forest approach. Int J Biol Sci, 14, 1669–1677.
9. Chen Z^†, Liu Xu^†, et al. (2018) Large-scale comparative assessment of computational predictors for lysine post-translational modification sites. Brief Bioinform. 20:2267–2290.
10. Chen Z^†, Zhao P^†, Li F et al. (2018) iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences, Bioinformatics;34:2499–2502.