•  副教授

陈培

发布时间:2021-09-06文章来源:华南理工大学数学学院浏览次数:2671

陈培


一、简历
       陈培
,副教授

       2021年9月至今,在华南理工大学数学学院工作,任副教授;

       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;

       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,获得博士学位;其中,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;

       2014年6月至2015年7月,在美国加利福尼亚州Quixey公司工作,算法工程师;

       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,获得硕士学位;

       20079月至2011年6月,在北京大学信息与技术科学学院读本科,获得学士学位;  

      

二、获奖情况

    [2]  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。

    [1]  获得2020-2021年度华南理工大学“优秀班主任”称号

  

主要研究方向:计算生物学与生物信息学(在计算数学方向招收硕士研究生)。


主要研究内容: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:大数据挖掘;高维短时间序列预测;复杂生物过程的临界状态预警;微生物群体分析等。


所在团队还可以招收博士研究生和博士后,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)的同学加入我们团队。欢迎已取得和将要取得博士学位的青年学者来我们团队做博士后,博士后待遇及其他详情请见:http://www2.scut.edu.cn/hr/2019/1016/c4457a340019/page.htm


  

一、承担基金项目情况        

主持基金项目:

    [5]  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)

    [4]  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)

    [3]  2021-2023,主持广东省自然科学基金面上基金项目(项目编号:2021A1515012317,已结题)

    [2]  2020-2021,主持博士后特别资助项目(项目编号:2020T130212,已结题)

    [1]  2019-2020,主持博士后面上基金项目(项目编号:2019M662895,已结题


参与基金项目:

    [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”重点研发专项“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);

    [1]  2020-2023,参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);

 

二、发表SCI论文情况   

[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. DOI:10.1093/bioinformatics/btac707  

[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. DOI:10.1093/jmcb/mjac052 

[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): 461474. 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, 2021:3057. 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):87DOI: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 Medicine2019, 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 ChenRui 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 ChenYongjun 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 ChenRui 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 LiuPei 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




chenpei(at)scut.edu.cn

华南理工大学4号楼