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. You can change the line of the save to export the model as a pickle 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
Academic Staff Member
Bioinformatics
Institute for Biochemistry and Biology
University of Potsdam
Potsdam,Germany