SNFM: A semi-supervised NMF algorithm for detecting biological functional modules

Mathematical Biosciences and Engineering

Xuezhong Zhou¹  Yutong Man²  Kuo Yang²  Guangming Liu³ 

[1] 1. Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China3. Data Center of Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, Beijing, China;1. Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China;2. School of Computer Science & Engineering, Xi’an University of Technology, Xi’an 710048, China;

关键词: ppi;    nmf;    semi-supervised;    functional modules;    dip;   

DOI  :  10.3934/mbe.2019094

来源: DOAJ

【 摘 要 】

Unraveling protein functional modules from protein-protein interaction networks is a crucial step to better understand cellular mechanisms. In the past decades, numerous algorithms have been proposed to identify potential protein functional modules or complexes from protein-protein interaction (PPI) networks. Unfortunately, the number of PPIs is rather limited, and some interactions are false positive. Therefore, the algorithms that only utilize PPI networks may not obtain the expected results related to functional modules. In this study, we propose a novel semi-supervised functional module detection method based on non-negative matrix factorization(NMF)(SNFM), which incorporate high-quality supervised PPI links from complexes as prior information.Our method outperforms all the other competitors with improvements on performance by around 15.4% in Precision, 28.9% in Recall, 27.1% in F-score (on DIP data set) by using PCDq as gold standards.

【 授权许可】

Unknown