Paper Info
Title
Virus-host Association Prediction by using Kernelized Logistic Matrix Factorization on Heterogeneous Networks
Abstract
Virus-host association studies are significant for understanding the complex functions and dynamics of microbial communities of human health or diseases. Several virus-host association prediction methods have been developed based on the information of sequences, virus networks, host networks and virus-host networks separately. In this study, we develop a heterogeneous network approach based on neighborhood regularization logistic matrix factorization (LMFH-VH) which integrate the virus similarity network and the host similarity network using known virus-host associations. The virus similarity network and the host similarity network were constructed based on oligonucleotide frequency measures and Gaussian interaction profile kernel similarity, respectively. LMFH-VH achieves the best performance on several validation datasets comparing with other four network-based methods. The host prediction accuracy of LMFH-VH is 24.17% and 12.8% higher than two recently proposed virus-host prediction methods, respectively. The codes and datasets are available at https://github.com/liudan111/LMFH-VH.git.
Year
DOI
Venue
2018
10.1109/BIBM.2018.8621214
2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Keywords
Field
DocType
Gaussian interaction profile,heterogeneous network,logistic matrix factorization,neighborhood regularization,oligonucleotide frequency,virus-host association
Kernel (linear algebra),Virus host,Computer science,Matrix decomposition,Regularization (mathematics),Gaussian,Artificial intelligence,Heterogeneous network,Machine learning,Human health
Conference
ISSN
ISBN
Citations 
2156-1125
978-1-5386-5489-7
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Dan Liu111519.90
Xiaohua Hu22819314.15
Xiaohua Hu32819314.15
Xingpeng Jiang43420.30