ILMF-VH

ILMF-VH (Kernelized Logistic Matrix Factorization with Integrating Different Information to Predict Potential Virus-Host Associations on the Heterogeneous Network) is a computational method for predicting associations between viruses and their hosts. It utilizes a heterogeneous network constructed by connecting a virus network with a host network based on known virus-host associations.

Key features of ILMF-VH:

1. The virus network is constructed using oligonucleotide frequency measurements.

2. The host network is built by integrating oligonucleotide frequency similarity and Gaussian interaction profile kernel similarity through similarity network fusion.

3. The method employs kernelized logistic matrix factorization to predict potential virus-host associations on the heterogeneous network.

Topic

Metagenomics;Microbiology;Metagenomic sequencing

Detail

  • Operation: Pathway or network prediction;Standardisation and normalisation;Data retrieval

  • Software interface: Command-line interface

  • Language: Python

  • License: Not stated

  • Cost: Free of charge

  • Version name: -

  • Credit: National Key Research and Development Program of China, National Natural Science Foundation of China.

  • Input: -

  • Output: -

  • Contact: Xingpeng Jiang xpjiang@mail.ccnu.edu.cn ,Tingting He tthe@mail.ccnu.edu.cn

  • Collection: -

  • Maturity: -

Publications

  • Predicting virus-host association by Kernelized logistic matrix factorization and similarity network fusion.
  • Liu D, et al. Predicting virus-host association by Kernelized logistic matrix factorization and similarity network fusion. Predicting virus-host association by Kernelized logistic matrix factorization and similarity network fusion. 2019; 20:594. doi: 10.1186/s12859-019-3082-0
  • https://doi.org/10.1186/S12859-019-3082-0
  • PMID: 31787095
  • PMC: PMC6886165

Download and documentation

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