Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Gene Systems Network Inferred from Expression Profiles in Hepatocellular Carcinogenesis by Graphical Gaussian Model | Hindawi Publishing Corporation EURASIP Journal on Bioinformatics and Systems Biology Volume 2007 Article ID 47214 11 pages doi 2007 47214 Research Article Gene Systems Network Inferred from Expression Profiles in Hepatocellular Carcinogenesis by Graphical Gaussian Model Sachiyo Aburatani 1 Fuyan Sun 1 Shigeru Saito 2 Masao Honda 3 Shu-ichi Kaneko 3 and Katsuhisa Horimoto1 1 Biological Network Team Computational Biology Research Center CBRC National Institute of Advanced Industrial Science and Technology AIST 2-42 Aomi Koto-ku Tokyo 135-0064 Japan 2 Chemo Bio Informatics Department INFOCOM CORPORATION Mitsui Sumitomo Insurance Surugadai Annex Building 3-11 Kanda-Surugadai Chiyoda-ku Tokyo 101-0062 Japan 3 Department of Gastroenterology Graduate School of Medical Science Kanazawa University 13-1 Takara-machi Kanazawa Ishikawa 920-8641 Japan Received 28 June 2006 Revised 27 February 2007 Accepted 1 May 2007 Recommended by Paul Dan Cristea Hepatocellular carcinoma HCC in a liver with advanced-stage chronic hepatitis C CHC is induced by hepatitis C virus which chronically infects about 170 million people worldwide. To elucidate the associations between gene groups in hepatocellular carcinogenesis we analyzed the profiles of the genes characteristically expressed in the CHC and HCC cell stages by a statistical method for inferring the network between gene systems based on the graphical Gaussian model. A systematic evaluation of the inferred network in terms of the biological knowledge revealed that the inferred network was strongly involved in the known genegene interactions with high significance P 10 4 and that the clusters characterized by different cancer-related responses were associated with those of the gene groups related to metabolic pathways and morphological events. Although some relationships in the network remain to be interpreted the analyses revealed a snapshot of the orchestrated expression of cancer-related groups and some pathways related with .
