Metadata-Version: 2.1
Name: TGPred
Version: 0.1.0
Summary: TGPred: Efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning, and optimization.
Home-page: https://github.com/tobefuture/TGPred
Author: Ling Zhang
Author-email: lingzhan@mtu.edu
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE

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# TGPred v.0.1.0 (Python Version)

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Python version of TGPred contains six efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning, and optimization:

- **HuberNet**: Huber loss function along with Network-based penalty function;
- **HuberLasso**: Huber loss function along with Lasso penalty function;
- **HuberENET**: Huber loss function along with Elastic Net penalty function;
- **MSENet**: Mean square error loss function along with Network-based penalty function;
- **MSELasso**: Mean square error loss function along with Lasso penalty function;
- **MSEENET**: Mean square error loss function along with Elastic Net penalty function;
- **APGD**: The Accelerated Proximal Gradient Descent (APGD) algorithm to solve the above six penalized regression models.

## Functions

Please refer from [Github site](https://github.com/tobefuture/TGPred).


