pydruglogics package

Subpackages

• pydruglogics.execution package
  • Submodules
    • pydruglogics.execution.Executor
      • train()
      • predict()
  • Module contents
• pydruglogics.input package
  • Submodules
    • pydruglogics.input.ModelOutputs
      • ModelOutputs.get_model_output()
      • ModelOutputs.size()
      • ModelOutputs.print()
    • pydruglogics.input.Perturbations
      • Perturbation.print()
    • pydruglogics.input.TrainingData
      • TrainingData.print()
      • TrainingData.size()
  • Module contents
• pydruglogics.model package
  • Submodules
    • pydruglogics.model.BooleanModel
      • BooleanModel.calculate_attractors()
      • BooleanModel.calculate_global_output()
      • BooleanModel.from_binary()
      • BooleanModel.to_binary()
      • BooleanModel.add_perturbations()
      • BooleanModel.print()
      • BooleanModel.clone()
      • BooleanModel.reset_attractors()
      • BooleanModel.has_attractors()
      • BooleanModel.has_stable_states()
      • BooleanModel.has_global_output()
      • BooleanModel.get_stable_states()
      • BooleanModel.model_name
      • BooleanModel.boolean_equations
      • BooleanModel.binary_boolean_equations
      • BooleanModel.mutation_type
      • BooleanModel.global_output
      • BooleanModel.attractors
      • BooleanModel.attractor_tool
      • BooleanModel.attractor_type
    • pydruglogics.model.BooleanModelOptimizer
      • BooleanModelOptimizer.save_to_file_models()
    • pydruglogics.model.Evolution
      • Evolution.calculate_fitness()
      • Evolution.create_initial_population()
      • Evolution.run()
      • Evolution.save_to_file_models()
    • pydruglogics.model.InteractionModel
      • InteractionModel.size()
      • InteractionModel.print()
      • InteractionModel.get_interaction()
      • InteractionModel.get_target()
      • InteractionModel.get_activating_regulators()
      • InteractionModel.get_inhibitory_regulators()
      • InteractionModel.model_name()
    • pydruglogics.model.ModelPredictions
      • ModelPredictions.run_simulations()
      • ModelPredictions.save_to_file_predictions()
      • ModelPredictions.get_prediction_matrix()
    • pydruglogics.model.Statistics
      • Statistics.sampling_with_ci()
      • Statistics.compare_two_simulations()
  • Module contents
• pydruglogics.utils package
  • Submodules
    • pydruglogics.utils.BNetworkUtil
      • get_file_extension()
      • remove_extension()
      • read_lines_from_file()
      • is_numeric_string()
      • parse_interaction()
    • pydruglogics.utils.Logger
      • set_logger()
    • pydruglogics.utils.PlotUtil
      • PlotUtil.plot_roc_and_pr_curve()
      • PlotUtil.plot_pr_curve_with_ci()
  • Module contents

Module contents

class pydruglogics.BooleanModel(model=None, file=None, attractor_tool='mpbn', attractor_type='stable_states', mutation_type='topology', model_name='', cloned_equations=None, cloned_binary_equations=None)

Bases: object

add_perturbations(perturbations)

property attractor_tool

property attractor_type

property attractors

property binary_boolean_equations

property boolean_equations

calculate_attractors(attractor_tool, attractor_type)

calculate_global_output(model_outputs, normalized=True)

clone()

from_binary(binary_representation, mutation_type)

get_stable_states()

property global_output

has_attractors()

has_global_output()

has_stable_states()

property model_name

property mutation_type

print()

reset_attractors()

to_binary(mutation_type)

class pydruglogics.Evolution(boolean_model=None, training_data=None, model_outputs=None, ga_args=None, ev_args=None)

Bases: BooleanModelOptimizer

property best_boolean_models

calculate_fitness(ga_instance, solutions, solution_idx)

create_initial_population(population_size, num_mutations, seed=None)

run()

save_to_file_models(base_folder='./results/models')

class pydruglogics.InteractionModel(interactions_file=None, model_name='', remove_self_regulated_interactions=False, remove_inputs=False, remove_outputs=False)

Bases: object

property all_targets

get_activating_regulators(index)

get_inhibitory_regulators(index)

get_interaction(index)

get_target(index)

property interactions

property model_name

print()

size()

class pydruglogics.Logger

Bases: object

classmethod set_logger(level=20, external_lib_off=True)

class pydruglogics.ModelOutputs(input_file=None, input_dictionary=None)

Bases: object

get_model_output(node_name)

property max_output

property min_output

property model_outputs

property node_names

print()

size()

class pydruglogics.ModelPredictions(boolean_models=None, perturbations=None, model_outputs=None, synergy_method='bliss', model_directory=None, attractor_tool=None, attractor_type=None)

Bases: object

get_prediction_matrix()

property predicted_synergy_scores

run_simulations(parallel=True, cores=4)

save_to_file_predictions(base_folder='./results/predictions')

class pydruglogics.Perturbation(drug_data=None, perturbation_data=None)

Bases: object

property drug_effects

property drug_names

property drug_targets

property drugs

property perturbations

print()

class pydruglogics.PlotUtil

Bases: object

static plot_pr_curve_with_ci(pr_df, auc_pr, boot_n, plot_discrete)

static plot_roc_and_pr_curve(predicted_synergy_scores, observed_synergy_scores, synergy_method, labels=None)

class pydruglogics.TrainingData(input_file=None, observations=None)

Bases: object

property observations

print()

property response

property responses

size()

property weight_sum

property weights

pydruglogics.compare_two_simulations(boolean_models1, boolean_models2, observed_synergy_scores, model_outputs, perturbations, synergy_method='bliss', label1='Models 1', label2='Models 2', normalized=True, plot=True, save_result=True)

pydruglogics.execute(train_params=None, predict_params=None)

pydruglogics.sampling_with_ci(boolean_models, observed_synergy_scores, model_outputs, perturbations, synergy_method='bliss', repeat_time=10, sub_ratio=0.8, boot_n=1000, confidence_level=0.9, plot=True, plot_discrete=False, save_result=True, with_seeds=True, seeds=42)

pydruglogics.train(*args, **kwargs)