- an application of the logistic regressor for the plant disease resistance genes.
- Given a fasta file and the corresponding expression file and a motif types which you think are associated with the plant disease resistance, if prepares the classification datasets and then fits a logistic regressor for the model building.
- You can visualize the model using the meshgrid and the np.array and it saves the model to the file.
- plant resistance gene logistic regressor: applying a logistic regressor specific for the training on the plant resistance genes.
- This applies the logistic regession based on the sequence characteristics of the plant disease resistance genes and the corresponding expression profile.
- A normalized log transformed expression methods can be used.@fasta_file: file containing the plant disease resistance genes, @expression_file: file containing the expression profile for those genes.
- It also takes a prediction motif profile which defines the presence and the absence of the resistance genes and makes a probability index.
- The function returns a accuracy score and writed the model to an output file. You can put a meshgrid to enable the visualization of the model using the np.array.
- You can change the save to a pickle file for the model
resistancegeneLogisticRegressor(fasta_file, \
expression_file, \
prediction_motif, \
prediction_size, \
output_file)
Gaurav Sablok
University of Potsdam
Potsdam,Germany