biopython

v1.71.0

biopython v1.71.0 Bio.NeuralNetwork.Gene.Schema.GeneticAlgorithmFinder

Find schemas using a genetic algorithm approach.

This approach to finding schema uses Genetic Algorithms to evolve a set of schema and find the best schema for a specific set of records.

The ‘default’ finder searches for ambiguous DNA elements. This can be overridden easily by creating a GeneticAlgorithmFinder with a different alphabet.

Link to this section Summary

Functions

__init__()

Initialize a finder to get schemas using Genetic Algorithms

_set_up_genetic_algorithm()

Set up the genetic algorithm parameters (PRIVATE)

find_schemas()

Find the given number of unique schemas using a genetic algorithm

Link to this section Functions

Link to this function __init__()

Initialize a finder to get schemas using Genetic Algorithms.

Arguments:

  • alphabet — The alphabet which specifies the contents of the schemas we’ll be generating. This alphabet must contain the attribute ‘alphabet_matches’, which is a dictionary specifying the potential ambiguities of each letter in the alphabet. These ambiguities will be used in building up the schema.
Link to this function _set_up_genetic_algorithm()

Set up the genetic algorithm parameters (PRIVATE).

This functions sole job is to set up the different genetic algorithm functionality. Since this can be quite complicated, this allows cusotmizablity of all of the parameters. If you want to customize specially, you can inherit from this class and override this function.

Link to this function find_schemas()

Find the given number of unique schemas using a genetic algorithm.

Arguments:

  • fitness - A callable object (ie. function) which will evaluate the fitness of a motif.
  • num_schemas - The number of unique schemas with good fitness that we want to generate.