DNN-QSAR-2019 | based Approaches to Overcome Feature Selection
kandi X-RAY | DNN-QSAR-2019 Summary
kandi X-RAY | DNN-QSAR-2019 Summary
DNN-QSAR-2019 is a Python library. DNN-QSAR-2019 has no bugs, it has no vulnerabilities and it has low support. However DNN-QSAR-2019 build file is not available. You can download it from GitHub.
Resources for "Neural-based Approaches to Overcome Feature Selection and Applicability Domain in Drug-related Property Prediction" - Sabando et al
Resources for "Neural-based Approaches to Overcome Feature Selection and Applicability Domain in Drug-related Property Prediction" - Sabando et al
Support
Quality
Security
License
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Support
DNN-QSAR-2019 has a low active ecosystem.
It has 2 star(s) with 1 fork(s). There are no watchers for this library.
It had no major release in the last 6 months.
DNN-QSAR-2019 has no issues reported. There are no pull requests.
It has a neutral sentiment in the developer community.
The latest version of DNN-QSAR-2019 is current.
Quality
DNN-QSAR-2019 has no bugs reported.
Security
DNN-QSAR-2019 has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
License
DNN-QSAR-2019 does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
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DNN-QSAR-2019 releases are not available. You will need to build from source code and install.
DNN-QSAR-2019 has no build file. You will be need to create the build yourself to build the component from source.
Top functions reviewed by kandi - BETA
kandi has reviewed DNN-QSAR-2019 and discovered the below as its top functions. This is intended to give you an instant insight into DNN-QSAR-2019 implemented functionality, and help decide if they suit your requirements.
- Calculate the next batch
- Computes the especificidad of a feature .
- r Sensibilidad sensitivity .
- Calculates the correct betaiones for a given probability t .
- Shuffle the next epoch .
Get all kandi verified functions for this library.
DNN-QSAR-2019 Key Features
No Key Features are available at this moment for DNN-QSAR-2019.
DNN-QSAR-2019 Examples and Code Snippets
No Code Snippets are available at this moment for DNN-QSAR-2019.
Community Discussions
No Community Discussions are available at this moment for DNN-QSAR-2019.Refer to stack overflow page for discussions.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install DNN-QSAR-2019
You can download it from GitHub.
You can use DNN-QSAR-2019 like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use DNN-QSAR-2019 like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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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