|
网络拓扑揭示了功能、疾病和系统发育
Network Topology Uncovers Function, Disease, and Phylogeny |
|
| |
| 课程网址: |
http://videolectures.net/prib2010_przulj_ntuf/
|
| 主讲教师: |
Nataša Pržulj
|
| 开课单位: |
帝国理工学院
|
| 开课时间: |
2010-10-21
|
| 课程语种: |
英语
|
| 中文简介: |
我们提出了新的工具，推动网络分析，以理解生物网络的结构。类似于分析和比较遗传序列的工具，我们正在开发解密大型网络数据集的新工具，目的是提高生物学理解并促进新疗法的开发。我们证明局部节点相似性对应于生物功能的相似性和疾病的参与。此外，我们引入了一个系统的，高度约束的网络局部结构测量，并证明蛋白质蛋白质相互作用（PPI）网络更好地通过几何图形建模，而不是任何以前的模型。通过证明PPI网络可以明确地嵌入到低维几何空间中并且构造它们的进化过程可以自然地在该空间中建模，进一步证实了几何模型。我们使用这些结果提出了一种新的PPI数据集去噪方法。此外，我们提出了新的网络对齐算法，这些算法仅基于网络拓扑，不仅能够进行功能预测，还能够重建系统发育。
|
| 课程简介: |
We present our new tools that are advancing network analysis towards a theoretical understanding of the structure of biological networks. Analogous to tools for analyzing and comparing genetic sequences, we are developing new tools that decipher large network data sets with the goal of improving biological understanding and contributing to development of new therapeutics. We demonstrate that local node similarity corresponds to similarity in biological function and involvement in disease. Also, we introduce a systematic, highly constraining measure of a network's local structure and demonstrate that protein-protein interaction (PPI) networks are better modeled by geometric graphs than by any previous model. The geometric model is further corroborated by demonstrating that PPI networks can explicitly be embedded into a low-dimensional geometric space and that evolutionary processes that constructed them can naturally be modelled in this space. We use these results to propose a new method for de-noising PPI data sets. Also, we present our new network alignment algorithms that are based only on network topology and are capable not only of function prediction, but also of reconstruction of phylogeny.
|
| 关 键 词: |
生物网络; 遗传序列; 网络数据集
|
| 课程来源: |
视频讲座网
|
| 最后编审: |
2020-01-16：chenxin
|
| 阅读次数: |
46
|
|
图 书 馆