期刊论文

Hu XiaoHua

英文

中国科学:生命科学(英文版)

1674-7305

2014

57

11

1115-1120

10.1007/s11427-014-4735-x

US National Science Foundation, Division of Industrial Innovation and PartnershipsNational Science Foundation (NSF) [1160960, 1332024]; Computing and Communication Foundations [0905291]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [90920005, 61170189]; Twelfth Five-year Plan of China [2012BAK24B01]; National Social Science Funds of China [122D223, 13ZD183]; Division of Computing and Communication FoundationsNational Science Foundation (NSF)NSF - Directorate for Computer & Information Science & Engineering (CISE) [0905291] Funding Source: National Science Foundation

本校为通讯机构

With the rapid accumulation of high-throughput metagenomic sequencing data, it is possible to infer microbial species relations in a microbial community systematically. In recent years, some approaches have been proposed for identifying microbial interaction network. These methods often focus on one dataset without considering the advantage of data integration. In this study, we propose to use a similarity network fusion (SNF) method to infer microbial relations. The SNF efficiently integrates the similarities of species derived from different datasets by a cross-network diffusion process. We ...