TGPred 0.1.0

TGPred: Efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning, and optimization.

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.