DeePaC-strain | DeePaC plugin shipping built-in models

by JakubBartoszewicz Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions Vulnerabilities Install Support

kandi X-RAY | DeePaC-strain Summary

DeePaC-strain is a Python library. DeePaC-strain has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub, GitLab.

DeePaC plugin shipping built-in models for predicting pathogenic potentials of novel strains of known bacterial species

Support

Quality

Security

License

Reuse

Support

DeePaC-strain has a low active ecosystem.

It has 0 star(s) with 0 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

DeePaC-strain has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of DeePaC-strain is current.

Quality

DeePaC-strain has no bugs reported.

Security

DeePaC-strain has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

DeePaC-strain is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

DeePaC-strain releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

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DeePaC-strain Key Features

No Key Features are available at this moment for DeePaC-strain.

DeePaC-strain Examples and Code Snippets

No Code Snippets are available at this moment for DeePaC-strain.

Community Discussions

No Community Discussions are available at this moment for DeePaC-strain.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install DeePaC-strain

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 GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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