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iPHoP: an integrated machine-learning framework to maximize host prediction for metagenome-assembled virus genomes
View ORCID ProfileSimon Roux, View ORCID ProfileAntonio Pedro Camargo, Felipe H. Coutinho, Shareef M. Dabdoub, Bas E. Dutilh, View ORCID ProfileStephen Nayfach, Andrew Tritt
doi: https://doi.org/10.1101/2022.07.28.501908
Simon Roux
1DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Antonio Pedro Camargo
1DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Felipe H. Coutinho
2Instituto de Ciencias del Mar (ICM-CSIC), Barcelona, Spain
Shareef M. Dabdoub
3Division of Biostatistics and Computational Biology, University of Iowa College of Dentistry, Iowa City, IA, USA
Bas E. Dutilh
4Institute of Biodiversity, Faculty of Biological Sciences, Cluster of Excellence Balance of the Microverse, Friedrich Schiller University, Rosalind Franklin Strasse 1, 07743, Jena, Germany
6Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
Stephen Nayfach
1DOE Joint Genome Institute, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Andrew Tritt
6Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Padualaan 8, 3584 CH, Utrecht, The Netherlands
Posted July 28, 2022.
iPHoP: an integrated machine-learning framework to maximize host prediction for metagenome-assembled virus genomes
Simon Roux, Antonio Pedro Camargo, Felipe H. Coutinho, Shareef M. Dabdoub, Bas E. Dutilh, Stephen Nayfach, Andrew Tritt
bioRxiv 2022.07.28.501908; doi: https://doi.org/10.1101/2022.07.28.501908
iPHoP: an integrated machine-learning framework to maximize host prediction for metagenome-assembled virus genomes
Simon Roux, Antonio Pedro Camargo, Felipe H. Coutinho, Shareef M. Dabdoub, Bas E. Dutilh, Stephen Nayfach, Andrew Tritt
bioRxiv 2022.07.28.501908; doi: https://doi.org/10.1101/2022.07.28.501908
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