https://github.com/liudan111/ILMF-VH
Kernelized Logistic Matrix Factorization based on Similarity Network Fusion for Predicting Virus-host Association
https://github.com/liudan111/ILMF-VH
Last synced: about 2 months ago
JSON representation
Kernelized Logistic Matrix Factorization based on Similarity Network Fusion for Predicting Virus-host Association
- Host: GitHub
- URL: https://github.com/liudan111/ILMF-VH
- Owner: liudan111
- Created: 2019-03-20T05:07:35.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2019-03-24T04:01:29.000Z (about 6 years ago)
- Last Synced: 2023-10-20T00:47:15.327Z (over 1 year ago)
- Language: Python
- Size: 15.3 MB
- Stars: 5
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-virome - INFH-VH - Integrating different features for virus-host prediction. [Python] (Host Prediction / RNA Virus Identification)
README
# IMFH-VH
Kernelized Logistic Matrix Factorization based on Similarity Network Fusion for Predicting Virus-host Association
===========
Xingpeng Jiang, [email protected]
Dan Liu, [email protected]
School of Computer,Central China Normal University Wuhan, China
For any questions regarding to this library, please feel free to contact the author.
Usage codes
---------------
code/dowload_host_of_virus.py is used to extract the "isolate host =" or "host" fields from the annotation files downloaded from complete viral genomes of NCBI
code/dowload_virus.py is used to retrieve the viral genomes from NCBI based on the accession numbers of viruses
code/similarity_os.py and code/main.py are used to calculate Gaussian interaction profile kernel similarity between hosts.
code/nrlmf.py are used to predict scores between viruses and hosts and code/PyDTI.py is main function which is applied to execute model simply
The codes of KBMF,NetLapRLS,BLM-NII ,CMF can refer to codes of Yong et al.https://github.com/stephenliu0423/PyDTI [3].
Note:the code is implemented by Python 2.7.9
Description of datasets
---------------
datasets/dataset I including 71 hosts and 352 viruses which only one specific host genome appeared in the prokaryotic genome database at NCBI[2].
datasets/dataset II including 2699 hosts and 820 viruses which is downloaded from RefSeq on 7/25/2015[1].
datasets/onf including onf measures of viruses and hosts which are obtained by Jie Ren's tools [2].
Note: Jie Ren's tools is used to get dissimilarity between viruses,you should transform to similarity.
Reference and Citation
------------
If you use this codes , please cite the following paper:
Kernelized Logistic Matrix Factorization based on Similarity Network Fusion for Predicting Virus-host Association
Our Reference
------------
[1]Edwards RA, McNair K, Faust K, Raes J, Dutilh BE, Smith M: Computational approaches to predict bacteriophage–host relationships. FEMS Microbiology Reviews 2016, 40(2):258-272.
[2]Ahlgren NA, Ren J, Lu YY, Fuhrman JA, Sun F: Alignment-free $d_2^*$ oligonucleotide frequency dissimilarity measure improves prediction of hosts from metagenomically-derived viral sequences. Nucleic Acids Research 2017, 45(1):39-53.
[3]Liu Y, Wu M, Miao C, Zhao P, Li XL: Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction. PLoS computational biology 2016, 12(2):e1004760.