图 书 馆
利用先验知识和/或不同实验条件的贝叶斯积分重建基因调控网络Gene regulatory network reconstruction by Bayesian integration of prior knowledge and/or different experimental conditions |
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| 课程网址: | http://videolectures.net/pim07_husmeier_grn/ |
| 主讲教师: | Dirk Husmeier |
| 开课单位: | 格拉斯哥大学 |
| 开课时间: | 2007-11-16 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | There have been various attempts to improve the reconstruction of gene regulatory networks from microarray data by the systematic integration of biological prior knowledge. Our approach follows the Bayesian paradigm where the prior knowledge is expressed in terms of energy functions, from which a prior distribution over network structures is obtained in the form of a Gibbs distribution. The hyperparameters of this distribution represent the weights associated with the prior knowledge relative to the data. We have derived and tested an MCMC scheme for sampling networks and hyperparameters simultaneously from the posterior distribution, thereby automatically learning how to trade off information from the prior and the data. We have extended this approach to a Bayesian coupling scheme for learning gene regulatory networks from a combination of related data sets that were obtained under different experimental conditions and are therefore potentially associated with different active subpathways. |
| 关 键 词: | 贝叶斯集成进行基因调控; 生物先验知识; 基因调控网络重构 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-10-28：lxf |
| 阅读次数: | 37 |