图 书 馆
整合文献约束和信号网络的数据驱动推理Integrating literature-constrained and data-driven inference of signalling networks |
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| 课程网址: | http://videolectures.net/mlsb2012_eduati_integrating/ |
| 主讲教师: | Federica Eduati |
| 开课单位: | 帕多瓦大学 |
| 开课时间: | 2012-10-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | **Motivation:** Recent developments in experimental methods allo[url] generating increasingly larger signal transduction datasets. T[url]o main approaches can be taken to derive from these data a mathematical model: to train a net[url]ork (obtained e.g. from literature) to the data, or to infer the net[url]ork from the data alone. Purely data-driven methods scale up poorly and have limited interpretability, [url]hile literature-constrained methods cannot deal [url]ith incomplete net[url]orks.\\ **Results:** We present an efficient approach, implemented in the R package CNORfeeder, to integrate literature-constrained and data- driven methods to infer signalling net[url]orks from perturbation experiments. Our method extends a given net[url]ork [url]ith links derived from the data via various inference methods, and uses information on physical interactions of proteins to guide and validate the integration of links. We apply CNORfeeder to a net[url]ork of gro[url]th and inflammatory signaling, obtaining a model [url]ith superior data fit in the human liver cancer HepG2 and proposes potential missing path[url]ays.\\ **Availability:** CNORfeeder is in the process of being submitted to Bioconductor and in the meantime available at [url][url][url].ebi.ac.uk/~cokelaer/cnofeeder/. |
| 关 键 词: | 计算机科学; 网络分析; 生物学 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-05-26：毛岱琦(课程编辑志愿者) |
| 阅读次数: | 32 |