Virus-host Association Prediction by using Kernelized Logistic Matrix Factorization on Heterogeneous Networks | IEEE Conference Publication | IEEE Xplore

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-ho...Show More

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.
Date of Conference: 03-06 December 2018
Date Added to IEEE Xplore: 24 January 2019
ISBN Information:
Conference Location: Madrid, Spain

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