TGPred: Efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning, and optimization.
Project description
TGPred v.0.1.0 (Python Version)
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.
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