Observed since 20 Oct, 19:48 UTC
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adpred
Prediction of Transcription Activation Domains from protein sequences
A tool for prediction of Transcription Activation Domains from protein sequences.
The main goal is to identify regions with high AD function probability in protein sequences. Moreover, at these observed regions, a saturated mutagenesis study can reveal insights into the important residues that confer the AD function to that region.
Contributions are welcome and encouraged.
ADpred is an open source software released under the MIT licence
Download counts and popularity will appear here as the collector receives them.
| Package / target | Requirement | Relationship |
|---|---|---|
kerasEnvironment{
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"marker": null,
"requires_python": ">=3.9",
"evaluation": "NOT_EVALUATED"
} | keras==3.11.3 | runtime |
numpyEnvironment{
"extras": "",
"marker": null,
"requires_python": ">=3.9",
"evaluation": "NOT_EVALUATED"
} | numpy==2.3.3 | runtime |
pandasEnvironment{
"extras": "",
"marker": null,
"requires_python": ">=3.9",
"evaluation": "NOT_EVALUATED"
} | pandas==2.3.2 | runtime |
plotlyEnvironment{
"extras": "",
"marker": null,
"requires_python": ">=3.9",
"evaluation": "NOT_EVALUATED"
} | plotly==6.3.0 | runtime |
requestsEnvironment{
"extras": "",
"marker": null,
"requires_python": ">=3.9",
"evaluation": "NOT_EVALUATED"
} | requests==2.32.5 | runtime |
requests-oauthlibEnvironment{
"extras": "",
"marker": null,
"requires_python": ">=3.9",
"evaluation": "NOT_EVALUATED"
} | requests-oauthlib==2.0.0 | runtime |
scikit-learnEnvironment{
"extras": "",
"marker": null,
"requires_python": ">=3.9",
"evaluation": "NOT_EVALUATED"
} | scikit-learn==1.7.2 | runtime |
tensorflowEnvironment{
"extras": "",
"marker": null,
"requires_python": ">=3.9",
"evaluation": "NOT_EVALUATED"
} | tensorflow==2.20.0 | runtime |
bdist_wheel · 309.65 KB · Python py3
sdist · 310.53 KB · Python source
Observed since 20 Oct, 19:48 UTC
No superseded metadata states collected. Unchanged observations do not create new revisions.
package_version:f796d540ecfdae8eed5bb2956a970fb58cd6e9d2cbeb06b9b30f18f9410c8846