Predicting virus-host association by Kernelized logistic matrix factorization and similarity network fusion.

ABSTRACT:

Background

Viruses are closely related to bacteria and human diseases. It is of great significance to predict associations between viruses and hosts for understanding the dynamics and complex functional networks in microbial community. With the rapid development of the metagenomics sequencing, some methods based on sequence similarity and genomic homology have been used to predict associations between viruses and hosts. However, the known virus-host association network was ignored in these methods.

Results

We proposed a kernelized logistic matrix factorization with integrating different information to predict potential virus-host associations on the heterogeneous network (ILMF-VH) which is constructed by connecting a virus network with a host network based on known virus-hos

SUBMITTER: Liu D 

PROVIDER: S-EPMC6886165 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature