|
生物网络动力学中的学习模式
Learning Patterns in the Dynamics of Biological Networks |
|
| |
| 课程网址: |
http://videolectures.net/kdd09_you_lpitd/
|
| 主讲教师: |
Chang hun You
|
| 开课单位: |
华盛顿大学
|
| 开课时间: |
2009-09-14
|
| 课程语种: |
英语
|
| 中文简介: |
我们开发了基于动态图的关系挖掘方法，以了解生物网络中随时间变化的结构模式。动态网络的分析不仅对了解系统层面的生命，而且对发现其他结构数据中的新模式也很重要。目前大多数基于图的数据挖掘方法忽略了生物网络的动态特性，因为它们只关注静态图。我们的方法分析一系列图，并发现捕获序列中图对之间发生变化的规则。这些规则表示图重写规则，第一个图必须通过这些规则才能与第二个图同构。然后，我们的方法将图形重写规则输入到机器学习系统中，该系统学习描述一类动态生物网络发生的变化类型的一般转换规则。发现的图形重写规则显示了生物网络如何随时间变化，而转换规则显示了结构变化中的重复模式。在本文中，我们将我们的方法应用于生物网络，以评估我们的方法，并了解生物系统如何随时间变化。我们使用覆盖率和预测指标评估我们的结果，并与生物学文献进行比较。
|
| 课程简介: |
Our dynamic graph-based relational mining approach has been developed to learn structural patterns in biological networks as they change over time. The analysis of dynamic networks is important not only to understand life at the system-level, but also to discover novel patterns in other structural data. Most current graph-based data mining approaches overlook dynamic features of biological networks, because they are focused on only static graphs. Our approach analyzes a sequence of graphs and discovers rules that capture the changes that occur between pairs of graphs in the sequence. These rules represent the graph rewrite rules that the first graph must go through to be isomorphic to the second graph. Then, our approach feeds the graph rewrite rules into a machine learning system that learns general transformation rules describing the types of changes that occur for a class of dynamic biological networks. The discovered graph-rewriting rules show how biological networks change over time, and the transformation rules show the repeated patterns in the structural changes. In this paper, we apply our approach to biological networks to evaluate our approach and to understand how the biosystems change over time. We evaluate our results using coverage and prediction metrics, and compare to biological literature.
|
| 关 键 词: |
动态图形; 生物网络; 数据挖掘方法; 机器学习系统
|
| 课程来源: |
视频讲座网
|
| 最后编审: |
2019-12-24：lxf
|
| 阅读次数: |
33
|
|
图 书 馆