10.5281/zenodo.5711877
https://zenodo.org/records/5711877
oai:zenodo.org:5711877
Jakub M. Bartoszewicz
Jakub M. Bartoszewicz
0000-0001-6893-2371
Hasso Plattner Institute
Ferdous Nasri
Ferdous Nasri
0000-0003-2018-4786
Hasso Plattner Institute
Melania Nowicka
Melania Nowicka
0000-0002-2403-1042
Hasso Plattner Institute
Bernhard Y. Renard
Bernhard Y. Renard
0000-0003-4589-9809
Hasso Plattner Institute
DeePaC models for novel fungal pathogens and real-time detection of multiple pathogen classes
Zenodo
2021
fungi
novel pathogens
bioinformatics
deep learning
next-generation sequencing
2021-11-18
10.5281/zenodo.5711876
v1.0
Creative Commons Attribution 4.0 International
A collection of DeePaC ResNet models for
1) pathogenic potential prediction for novel fungal species (input Illumina read length: 250bp)
2) real-time detection of novel bacterial, viral and fungal pathogens (input Illumina read length: 25-250bp). Those models assume four classes: non-pathogens (i.e. commensal bacteria or non-human viruses), pathogenic bacteria, human-infecting viruses, and human-infecting fungi. Two alternative models are provided: we recommend either using the 'log' model (for faster inference), or an ensemble averaging predictions of both models (for better results).
See the code and manual at https://gitlab.com/dacs-hpi/deepac. Model weights for the fungal (-fun-) and multi-class (-multi4-) models in .h5 files and config .ini files.
The models were trained on read sets hosted here: https://zenodo.org/record/5713153 based on a curated database of pathogenic fungi (https://zenodo.org/record/5711852).
See also the preprint: https://www.biorxiv.org/content/10.1101/2021.11.30.470625v1