更新日期:2024年9月9日
个人简介
陈培,教授,博士生导师。 主要从事计算生物学、人工智能算法的研究。 在Nature Communications、PNAS、Science Bulletin、Bioinformatics等国际顶级期刊上发表论文20余篇。主持国家自然科学基金优秀青年基金、面上项目,广东省面上项目,中国博士后特别资助项目等基金。
工作经历
2018年7月至2021年7月,在华南理工大学数学学院做博士后,其中于2020年11月评为博士后副研究员;
2021年9月-2023年10月,在华南理工大学数学学院,任副教授;
2023年10月- 至今,在华南理工大学数学学院,任教授;
教育经历
2007年9月至2011年6月,在北京大学信息与技术科学学院读本科,获得理学学士学位;
2011年9月至2014年6月,在北京大学信息与技术科学学院读硕士,获得理学硕士学位;
2015年9月至2018年7月,在华南理工大学计算机工程与技术学院读博士,获得理学博士学位;
2017年10月至2018年10月,在美国加州大学洛杉矶分校医学院,国家留学基金委博士联合培养;
获奖、荣誉称号
获得广东省大学生“挑战杯”学术科技竞赛“优秀指导教师” 称号,所指导的作品获得第十六届“挑战杯”广东大学生课外学术科技作品竞赛特等奖。
研究领域
计算生物学与生物信息学。主要研究高维非线性动力系统的数据挖掘与信息分析,致力于把数学理论和计算机科学的方法应用于解决实际问题,包括:高维短时间序列预测;复杂疾病的恶性突变预警;微生物群体分析等。
欢迎对数据挖掘与分析、网络模型、机器学习和深度学习方法感兴趣,并具有一定编程基础(例如MATLAB、R或Python等)的同学报考我们组的硕士或博士研究生。
科研项目
主持基金项目:
[5] 2024-2026,主持国家自然科学基金委优秀青年基金项目“计算系统生物学”(项目编号:12322119);
[4] 2023-2026,主持国家自然科学基金委面上基金项目“基于时空信息转换的生物系统临界状态预警方法的研究”(项目编号:12271180);
[3] 2020-2022,主持国家自然科学基金委青年基金项目“基于数据挖掘的结直肠癌临界点的预警算法”(项目编号:11901203,已结题);
[2] 2021-2023,主持广东省自然科学基金面上基金项目“复杂疾病恶性突变的分子网络动态行为与预警信号分析”(项目编号:2021A1515012317,已结题);
[1] 2020-2021,主持博士后特别资助项目(项目编号:2020T130212,已结题);
参与重点基金项目:
[3] 2022-2025,参与国家自然科学基金委重点项目“基于多模态数据的健康临界状态的数学刻画与预警理论” (项目编号:12131020) ;
[2] 2021-2022,参与国家自然科学基金委“数学与医疗健康交叉”重点专项“典型肺疾病的早期预警、病程演进建模与治疗方案优化” (项目编号:12026608,已结题);
[1] 2020-2023,参与国家自然科学基金委重点项目“2型糖尿病发生发展过程的临界状态预测理论和生物信息学方法”(项目编号:31930022,已结题);
发表论文
[29] Jiayuan Zhong, Hui Tang, Ziyi Huang, Hua Chai, Fei Ling*, Pei Chen*, Rui Liu*. Uncovering the Pre-Deterioration State during Disease Progression Based on Sample-Specific Causality Network Entropy (SCNE). Research, 2024, 7:0368. DOI:10.34133/research.0368 2024_Research.pdf
[28] Hao Peng, Wei Wang, Pei Chen*, Rui Liu*, DEFM: Delay-embedding-based forecast machine for time series forecasting by spatiotemporal information transformation. Chaos, 2024, 34. DOI:10.1063/5.0181791 Chaos2024.pdf
[27] Renhao Hong, Yuyan Tong, Huisheng Liu, Pei Chen*, Rui Liu*. Edge-based relative entropy as a sensitive indicator of critical transitions in biological systems. Journal of Translational Medicine, 2024, 22:333. DOI:10.1186/s12967-024-05145-3 JTM2024.pdf
[26] Yuyan Tong, Renhao Hong, Ze Zhang, Kazuyuki Aihara, Pei Chen*, Rui Liu*, Luonan Chen*. Earthquake alerting based on spatial geodetic data by spatiotemporal information transformation learning. Proceedings of the National Academy of Sciences, USA, 2023, 120(37):e2302275120. DOI:10.1073/pnas.2302275120 2023_PNAS.pdf
[25] Hao Peng, Pei Chen*, Rui Liu*, Luonan Chen*. Spatiotemporal information conversion machine for time-series forecasting. Fundamental Research, 2023, DOI:10.1016/j.fmre.2022.12.009
[24] Jiayuan Zhong, Dandan Ding, Juntan Liu, Rui Liu, Pei Chen*. SPNE: sample-perturbed network entropy for revealing critical states of complex biological systems. Briefings in Bioinformatics, 2023, 24(2):bbad028. DOI:10.1093/bib/bbad028 2023_BIB_SPNE.pdf
[23] Jiayuan Zhong, Chongyin Han, Yangkai Wang, Pei Chen*, Rui Liu*. Identifying the critical state of complex biological systems by the directed-network rank score method. Bioinformatics, 2022, 38(24): 5398–5405. DOI:10.1093/bioinformatics/btac707 2022_Bioinformatics.pdf
[22] Pei Chen, Jiayuan Zhong, Kun Yang, Xuhang Zhang, Yingqi Chen, Rui Liu*. TPD: a web tool for tipping-point detection based on dynamic network biomarker. Briefings in Bioinformatics, 2022, 23(5): bbac399. DOI:10.1093/bib/bbac399 2022_BIB_TPD.pdf
[21] Hao Peng, Jiayuan Zhong, Pei Chen*, Rui Liu*. Identifying the critical states of complex diseases by the dynamic change of multivariate distribution. Briefings in Bioinformatics, 2022, 23(5): bbac177. DOI:10.1093/bib/bbac177 2022BIB_KL.pdf
[20] Chongyin Han, Jiayuan Zhong, Qinqin Zhang, Jiaqi Hu, Rui Liu, Huisheng Liu, Zongchao Mo, Pei Chen*, Fei Ling*.
Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development. Computational and Structural Biotechnology Journal. 2022, 20: 1189–1197. DOI:10.1016/j.csbj.2022.02.019 2022CSBJ.pdf
[19] Jiayuan Zhong, Huisheng Liu, Pei Chen*. Single-sample network module biomarkers (sNMB) reveals the pre-deterioration stage of disease progression. Journal of Molecular Cell Biology, 2022, 14(8):mjac052. DOI:10.1093/jmcb/mjac052 2022_JMCB.pdf
[18] Rui Liu, Jiayuan Zhong, Renhao Hong, Ely Chen, Kazuyuki Aihara, Pei Chen*, Luonan Chen*. Predicting local COVID-19 outbreaks and infectious disease epidemics based on landscape network entropy. Science Bulletin, 2021. 66(22): 2265–2270. DOI:10.1016/j.scib.2021.03.02220 2021_ScienceBulletin.pdf
[17] Jiayuan Zhong, Chongyin Han, Xuhang Zhang, Pei Chen*, Rui Liu*. SGE: Predicting cell fate commitment during early embryonic development by single-cell graph entropy. Genomics, Proteomics & Bioinformatics, 2021, 19(3): 461–474. DOI:10.1016/j.gpb.2020.11.008 2021GPB.pdf
[16] Jiaqi Hu, Chongyin Han, Jiayuan Zhong, Huisheng Liu, Rui Liu, Wei Luo*, Pei Chen*, Fei Ling*. Dynamic network biomarker of pre-exhausted CD8+ T cells contributed to T cell exhaustion in colorectal cancer. Frontiers in Immunology, 2021, 12:691142. DOI:10.3389/fimmu.2021.691142 2021_Frontiers in immunology.pdf
[15] Min Dong, Xuhang Zhang, Kun Yang, Rui Liu*, Pei Chen*. Forecasting the COVID-19 transmission in Italy based on the minimum spanning tree of dynamic region network. PeerJ, 2021, 9: e11603. DOI:10.7717/peerj.11603 2021peerj.pdf
[14] Xuhang Zhang, Rong Xie, Zhengrong Liu, Yucong Pan, Rui Liu*, Pei Chen*. Identifying pre-outbreak signals of hand, foot and mouth disease based on landscape dynamic network marker. BMC Infectious Diseases, 2021, 21:6. DOI:10.1186/s12879-020-05709-w 2021_BMC Infect Dis.pdf
[13] Pei Chen, Rui Liu*, Kazuyuki Aihara, Luonan Chen*. Autoreservoir computing for multistep ahead prediction based on the spatiotemporal information transformation. Nature Communications, 2020, 11:4568. DOI:10.1038/s41467-020-18381-0 2020_NC_ARNN.pdf
[12] Rui Liu, Pei Chen*, Luonan Chen*. Single-sample landscape entropy reveals the imminent phase transition during disease progression. Bioinformatics, 2020, 36(5): 1522-1532. DOI:10.1093/bioinformatics/btz758 2020_Bioinformatics.pdf
[11] Jiayuan Zhong, Rui Liu*, Pei Chen*. Identifying critical state of complex diseases by single-sample Kullback–Leibler divergence, BMC Genomics, 2020, 21(1):87. DOI:10.1186/s12864-020-6490-7 2020_BMCgenomics.pdf
[10] Pei Chen, Shuo Li, Wenyuan Li, Jie Ren, Fengzhu Sun, Rui Liu*, Xianghong Jasmine Zhou*. Rapid diagnosis and comprehensive bacteria profiling of sepsis based on cell-free DNA. Journal of Translational Medicine, 2020, 18:5. DOI:10.1186/s12967-019-02186-x 2020_JTM.pdf
[9] Yingqi Chen, Kun Yang, Jialiu Xie, Rong Xie, Zhengrong Liu, Rui Liu*, Pei Chen*. Detecting the outbreak of influenza based on the shortest path of dynamic city network. PeerJ, 2020, 8: e9432. DOI:10.7717/peerj.9432 2020_PeerJ.pdf
[8] Kun Yang, Jialiu Xie, Rong Xie, Yucong Pan, Rui Liu*, Pei Chen*. Real-time forecast of influenza outbreak using dynamic network marker based on minimum spanning tree. BioMed Research International, 2020, 2020:7351398, 1-11. DOI:10.1155/2020/7351398 2020_BioMed Research International.pdf
[7] Pei Chen, Ely Chen, Luonan Chen, Xianghong Jasmine Zhou, Rui Liu*. Detecting early-warning signals of influenza outbreak based on dynamic network marker. Journal of Cellular and Molecular Medicine, 2019, 23(1):395–404. DOI:10.1111/jcmm.13943 2019_JCMM.pdf
[6] Rui Liu, Jiayuan Zhong, Xiangtian Yu, Yongjun Li, Pei Chen*. Identifying critical state of complex diseases by single-sample-based hidden Markov model, Frontiers in Genetics, 2019, 10: 285. DOI:10.3389/fgene.2019.00285 2019_Frontiers_genetic.pdf
[5] Pei Chen, Yongjun Li, Xiaoping Liu, Rui Liu*, Luonan Chen*. Detecting the tipping points in a three-state model of complex diseases by temporal differential networks. Journal of Translational Medicine, 2017, 15(1): 217. DOI:10.1186/s12967-017-1320-7 2017JTM.pdf
[4] Pei Chen, Rui Liu, Yongjun Li, Luonan Chen. Detecting critical state before phase transition of complex biological systems by hidden Markov model. Bioinformatics, 2016, 32(14): 2143-2150. DOI:10.1093/bioinformatics/btw154 2016Bioinformatics.pdf
[3] Pei Chen, Yongjun Li. The decrease of consistence probability: at the crossroad of catastrophic transition of a biological system. BMC systems biology. 2016, 10(2): 139-150. 2016BMC-systembiology.pdf
[2] Pei Chen, Rui Liu, Kazuyuki Aihara, Luonan Chen. Identifying critical differentiation state of MCF-7 cells for breast cancer by dynamical network biomarkers. Frontiers in Genetics. 2015, 28;6:252. DOI:10.3389/fgene.2015.00252 2015Frontiers_in_genetics.pdf
[1] Rui Liu, Pei Chen, Kazuyuki Aihara, Luonan Chen. Identifying early-warning signals of critical transitions with strong noise by dynamical network markers. Scientific Reports, 2015, 5:1-13. DOI:10.1038/srep17501 2015_Srep.pdf
教学活动
本科生课程: 数据结构、数据结构课程设计
我的团队
(1) 刘锐; 关晓玲; 陈培; 中国, 201810017326.3, 李明亮 ; 一种基于隐马尔科夫模型检测复杂生物系统相变临界点的方法, 2018 (专利)
(2) 张旭航; 刘锐; 陈培; 钟佳元 ; 中国, CN202010730553.8. 一种基于动态网络标志物的区域性传染病疫情预警方法, 2020-7-27, (专利)
(3) 刘锐; 王俊霞; 陈培 ; 一种基于相对熵指标检测复杂生物系统相变临界点的方法, 2020-1-10, 中国, CN202010025627.8 (专利)
(4) 刘锐; 钟佳元; 马硕; 金海洋; 陈培 ; 基于单样本sKLD指标检测复杂生物系统相变临界点的方法, 20 19-11-20, 中国, CN201911142801.0 (专利)