Prediction of Hot Regions in PPIs Based on Improved Local Community Structure Detecting
Prediction of Hot Regions in PPIs Based on Improved Local Community Structure Detecting
复制标题
基于改进局部群落结构检测的PPI热点区域预测
DOI：
10.1109/tcbb.2018.2793858
复制
发表时间：
2018-09-01
影响因子：
4.5
通讯作者：
Zhang, Xiaolong
中科院分区：
文献类型：
--
作者：
Lin, Xiaoli;Zhang, Xiaolong
PPI中的热区是由紧密排列的热点组成的组装区。热区的发现有助于了解生命活动，具有重要的生物学应用价值。热点区域的识别是蛋白质设计和癌症预防的基础。现有的热点区域预测算法往往存在预测精度不高、不稳定等缺陷。提出了一种基于生物多样性特征的热点区域预测方法。首先，使用改进的mRMR方法进行特征评估。然后，采用支持向量机建立分类模型的基础上选择的特征。此外，提出了一种新的聚类算法LCSD（Local community structure detecting），用于检测和分析热点区域的构象。在聚类过程中，蛋白质残基的链接相似性被引入到处理边界节点。该算法能有效地处理缺失的剩余节点，并控制局部社区边界。结果表明，该方法能更有效地获得热点区域的空间结构，并且比以往的方法更有效地精确识别热点区域。
The hot regions in PPIs are some assembly regions which are composed of the tightly packed HotSpots. The discovery of hot regions helps to understand life activities and has very important value for biological applications. The identification of hot regions is the basis for protein design and cancer prevention. The existing algorithms of predicting hot regions often have some defects, such as low accuracy and unstability. This paper proposes a novel hot region prediction method based on diverse biological characteristics. First, feature evaluation is employed by using an impoved mRMR method. Then, SVM is adopted to create cassification model based on the features selected. In addition, a new clustering algorithm, namely LCSD (Local community structure detecting), is developed to detect and analyze the conformation of hot regions. In the clustering process, the link similarity of protein residues is introduced to handle the boundary nodes. This algorithm can effectively deal with the missing residue nodes and control the local community boundaries. The results indicate that the spatial structure of hot regions can be obtained more effectively, and that our method is more effective than previous methods for precise identification of hot regions.