Foundations of Computational Knowledge Discovery from cDNA Microarray Data

从 cDNA 微阵列数据发现计算知识的基础

• 批准号: 12480080
• 负责人: MIYANO Satoru
• 金额: $ 9.22万
• 依托单位: The University of Tokyo
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (B)
• 财政年份: 2000
• 资助国家: 日本
• 起止时间: 2000 至 2002
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-12480080/
• 关键词: Gene network; Knowledge discovery; Qualitative network; Boolean network; Microarray; Hybrid Petri net; Systems biology; Computational learning; ブーリアンネットワーク

Since the beginning of this research project, the cDNA microarrays have been intensively employed for measuring gene expressions in laboratories. Some projects on genome-wide gene expression analysis have planed where commercialized statistical analysis program packages and databases are employed as bioinformatics tools in an ad hoc way. However, foundations based on information science were not paid attentions so much while only software tools have been developed from the viewpoint of practice in Japan.This reseach project has contributed to the development of novel microarray data analysis technology. First, we have created a mathematical framework for gene network models from cDNA microarray data (Boolean network model, Bayesian network model, system of ordinary differential equations), then we have developed computational methods for inferring these models from cDNA microarray data. These methods were examined through real biological analyzes. The second contribution is a development of modeling and simulation technology for gene networks (an extention of hybrid Petri net and model simulation of various biopathways).With these research contributions, we have established a computational strategy for developing systems biology.

自本研究项目开始以来，基因芯片已被广泛应用于实验室中的基因表达检测。一些关于全基因组基因表达分析的项目已经计划，其中商业化的统计分析程序包和数据库以特别的方式用作生物信息学工具。然而，基于信息科学的基础并没有得到太多的重视，日本只从实践的角度开发了软件工具，这一研究项目为新型微阵列数据分析技术的发展做出了贡献。首先，我们建立了基于基因芯片数据的基因网络模型(布尔网络模型、贝叶斯网络模型、常微分方程组)的数学框架，然后发展了从基因芯片数据推断这些模型的计算方法。这些方法通过实际的生物分析进行了检验。第二个贡献是对基因网络建模和仿真技术的发展(混合Petri网的扩展和各种生物路径的模型仿真)。通过这些研究贡献，我们建立了发展系统生物学的计算策略。

项目成果

Kim, S., Imoto, S., Miyano, S.: "Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data"Lecture Note in Computer Science. 2602. 104-113 (2003)

Kim, S.、Imoto, S.、Miyano, S.：“动态贝叶斯网络和非参数回归，用于根据时间序列基因表达数据对基因网络进行非线性建模”计算机科学讲义。

Kim, S., Imoto, S., Miyano, S: "Dynamic Bayesian network and nonparametric regression for nonlinear modeling of gene networks from time series gene expression data"Lecture Note in Computer Science. 2602. 104-113 (2003)

Kim, S.、Imoto, S.、Miyano, S：“动态贝叶斯网络和非参数回归，用于根据时间序列基因表达数据对基因网络进行非线性建模”计算机科学讲义。

H. Matsuno, R. Murakami, R. Yamane, N.Yamasaki, S. Fujita, H. Yoshimori, and S. Miyano: "Boundary formation by notch signaling in Drosophila multicellular systems : experimental observations and a gene network modeling by Genomic Object Net"Proc. Pacific

H. Matsuno、R. Murakami、R. Yamane、N.Yamasaki、S. Fujita、H. Yoshimori 和 S. Miyano：“果蝇多细胞系统中缺口信号传导的边界形成：实验观察和基因组对象的基因网络建模

Matsuno, H.: "XML documentation of biopathways and their simulations in Genomic Object Net"Genome Infbrmatics. 12. 54-62 (2001)

Matsuno, H.：“生​​物途径的 XML 文档及其在基因组对象网络中的模拟”基因组信息学。

Akutsu, T.: "Selecting informative genes for cancer classification using gene expression data"Proc. IEEE-EURASIP Workshop on Non-linear Signal and Image Processing NSIP-01. 1-6 (2001)

Akutsu，T.：“使用基因表达数据选择用于癌症分类的信息基因”Proc。