deepac-vir | DeePaC plugin shipping built-in models for novel human
kandi X-RAY | DeePaC-vir Summary
kandi X-RAY | DeePaC-vir Summary
DeePaC-vir is a Python library. DeePaC-vir has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However DeePaC-vir build file is not available. You can download it from GitLab.
DeePaC plugin shipping built-in models for novel human virus detection
DeePaC plugin shipping built-in models for novel human virus detection
Support
Quality
Security
License
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Support
DeePaC-vir has a low active ecosystem.
It has 0 star(s) with 0 fork(s). There are no watchers for this library.
It had no major release in the last 12 months.
DeePaC-vir has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of DeePaC-vir is 0.2.0
Quality
DeePaC-vir has no bugs reported.
Security
DeePaC-vir has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
DeePaC-vir is licensed under the MIT License License. This license is Permissive.
Permissive licenses have the least restrictions, and you can use them in most projects.
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DeePaC-vir releases are available to install and integrate.
DeePaC-vir has no build file. You will be need to create the build yourself to build the component from source.
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Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of deepac-vir
Currently covering the most popular Java, JavaScript and Python libraries. See a Sample of deepac-vir
DeePaC-vir Key Features
No Key Features are available at this moment for DeePaC-vir.
DeePaC-vir Examples and Code Snippets
No Code Snippets are available at this moment for DeePaC-vir.
Community Discussions
No Community Discussions are available at this moment for DeePaC-vir.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install DeePaC-vir
We recommend using Bioconda (based on the conda package manager) or custom Docker images based on official Tensorflow images. Alternatively, a pip installation is possible as well.
Support
For any new features, suggestions and bugs create an issue on GitLab.
If you have any questions check and ask questions on community page Stack Overflow .
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