摘要： 现有采用机器学习方法的蛋白质交互关系识别系统仅以单句为依据,并且存在标注数据缺乏导致训练集规模小的问题。为此,基于相似性混合模型提出一种新的蛋白质交互识别方法。采用基本的关系相似性(RS)模型做初始判断,利用大规模文本计算单词特征间的相似性,在基本RS模型的基础上通过特征聚类方式引入单词相似性模型,从而建立一个混合模型。实验结果表明,该方法能够取得较高且较均衡的精确度和召回率,而单词相似性的引入又进一步提高了F值,并且其直接利用已有的交互信息,可避免额外的人工标注。

关键词: 蛋白质交互, 关系相似性, 单词相似性, K近邻分类, 层次聚类

Abstract: Current machine learning-based Protein-protein Interaction(PPI)identification systems make predictions solely on evidence within a single sentence and suffer from small training set.In this paper,a hybrid similarity model-based approach is proposed to address these issues.A basic Relational Similarity(RS) model is established to make initial predictions.Word similarity matrices are constructed using a corpus-based approach.A clustering algorithm is applied to group words according to their similarity.The obtained word clusters are introduced to the basic RS model to build a hybrid model.Experimental results show that the basic RS model achieves higher and well-balanced precision and recall,and the introduction of the word similarity model further improves the F-score.This approach makes use of known PPI information,thus releases the burden of manual annotation.

Key words: Protein-protein Interaction(PPI), Relational Similarity(RS), word similarity, K-nearest Neighbor(KNN) classification, hierarchical clustering

中图分类号: 

• TP391