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姓 名:
Christine Nardini
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86 21 54920451
上海市岳阳路320号 200031

University qualification •2003-2005 - PhD in Electronics and Informatics at University of Bologna. Supervisor: Prof. Luca Benini. •2003-2004 – Visiting researcher at the Computer Science Department at Stanford University (CA, USA), with Prof Giovanni de Micheli. For the development of data mining algorithms to be applied to microarrays for gene expression analyses. •1999 National Habilitation to Engineers Professional Activity. •1992-1998 –Master Degree in Electrical Engineering – Biomedical specialization at the University of Bologna (90/100). Tutor: Prof. Mauro Ursino •1992 – BS Degree at Istituto N. Copernico in Bologna (60/60). Employment record since 02/2008 Research group leader of the Clinical Genomic Network group, in a joint appointment of the Max-Planck-Society and the Chinese Academy of Sciences, at the PICB, Shanghai. 2007. Collaborator at the Telethon Institute for Genetics and Medicine (Naples, Italy) with the Systems Biology Lab 2006- 2007. Research Assistant at the Department of Electronic Informatics and Systems (DEIS) at the University of Bologna (Italy). 2006-2008. Professor for the module of systems biology-biosensors at the International Master of Bioinformatics, University of Bologna, Italy. 1999 - 2002. Field Engineer in the Cardiac Rhythm Management Division at Medtronic S.p.A. for assistance during implant and follow-up of patients with implantable devices for brady/tachy arrhythmias. Education activity on brady/tachy arrhythmias and cardiac stimulation for nurses and medical doctors in various hospitals in the regions of Emilia Romagna and Marche. Teaching experience Systems biology module in the course Biosensors at the International Master of Bioinformatics, University of Bologna, Italy Thank to the innovative approaches to genetic (genomics), medicine is facing the tangible possibility to shift its conventional approach toward personalized medicine. This is an evidence based science, able to improve dramatically the efficiency of prevention and treatments by reducing side effects and empowering therapeutic approaches. This science is rooted in the concept of genomic profile, that is the ensemble not only of specific genes variants (genotype), but also of specific patterns of (disrupted) genes' activation, typical of each unique patient-and-disease complex. Knowing the genomic profile of an individual in a given condition might allow to define a better targeted therapeutic approach. However, the identification of disrupted patterns in a genomic profile is extremely complex, and different approaches as well as different samples under study can highlight different and sometimes non-overlapping sets of genes responsible for the complex changes that are undergoing in a cell during a disease. Thus, the translation of these concepts into medical practice represents still and important challenge. Our research focuses on the identification of approaches that try to resolve such apparent incongruities, notably using gene network reverse engineering approaches and systemic approaches, in strong connection with the use of experimental data. The objective is two-fold: * identify approaches that can use diverse gene signatures as a source of information enrichment to build more complex, multidimensional (networks vs lists) gene signatures * use these approaches to get insight into the functioning of innovative/alternative medical approaches


自身免疫性疾病是一种快速发展的复杂性疾病,目前其发病机制还不清楚,并且当前的各种西药治疗手段都有让患者难以接受的副作用,这大大降低了患者自身的免疫系统抵抗疾病的能力。 因此,阐明自身免疫性疾病发生发展的分子机制,了解疾病发展过程中发生改变的各种复杂的细胞信号传导通路,对于提高此类疾病的治疗水平是至关重要的。通过高通量的分子生物学实验方法产生可以应用于计算基因组学分析的数据,能够有效的从基因组水平系统地诠释与此类疾病相关的信号传导通路。这种研究方法不仅可以同时比较不同的治疗手段以找到更为有效的新的治疗方法,还可以减少药物治疗过程中所产生的各种副作用,从而加速患者的康复过程,为新药研发开辟了道路。




 [1] C. Guiducci, C. Nardini, High Parallelism, Portability and Broad Accessibility: Technologies for Genomics, ACM Journal on Emerging Technologies in Computing Systems, 2008. 

[2] C. Nardini, M. D. Kuo and L. Benini, Statistical Significance in omic data analyses, Proceedings of International Conference on bio-inspired systems and signal processing Biosignals2008, Portugal. 

[3] D. Masotti, C. Nardini, S. Rossi, E. Bonora, G. Romeo, S. Volinia and L. Benini, TOM: enhancement and extension of a tool suite for in silico approaches to multigenic complex disorders, Bioinformatics, 2007, doi: 10.1093/bioinformatics/btm588. 

[4] C. Nardini, L. Benini and G. de Micheli, Circuits and Systems for High-Throughput Biology, IEEE Circuits and Systems Magazine, 2006, 6(3), pp.10-20. 

[5] C. Nardini, D. Masotti, S. Yoon, E. Macii, M.D. Kuo, G. de Micheli and L. Benini, Mining gene sets for measuring similarities, Proceedings of IEEE Symposium on Computers and Communications (ISCC), 2006. 

[6] M. Diehn, C. Nardini, D.S. Wang, A. Hsiao, S. Cha and M.D. Kuo Neuroimaging Characteristics Reflect Tumor Gene Expression Signatures and Predict Survival in Glioblastoma Multiforme, International Journal of Radiation Oncology Biology Physics, Volume 66, Issue 3, Supplement 1, 2006, Page S18 

[7] S. Rossi-D. Masotti, C. Nardini, E. Bonora, G. Romeo, E. Macii, L. Benini and S. Volinia, TOM: a web-based integrated approach for efficient identification of candidate disease genes, Nucleic Acids Res., 34: W285 - W292; doi:10.1093/nar/gkl340. 

[8] S. Yoon, C. Nardini, L. Benini and G. de Micheli, Discovering coherent biclusters from gene Expression Data Using Zero-Suppressed Binary Decision Diagrams, The IEEE/ACM Transaction on Computational Biology and Bioinformatics, Vol. 3, N. 2, 2005. 

[9] S. Yoon, C. Nardini, L. Benini and G. de Micheli. Enhanced pClustering and its applications to gene expression data, In Proceedings of IEEE Symposium on BIBE (Bioinformatics and Bioengineering), pp. 275-282, 2004. 

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