Automated discovery of detectors and iteration-performing calculations to recognize patterns in protein sequences using genetic programming

This paper describes an automated process for the dynamic creation of a pattern-recognizing computer program consisting of initially unknown detectors, an initially-unknown iterative calculation incorporating the as-yet-uncreated detectors, and an initially-unspecified final calculation incorporating the results of the as-yet-uncreated iteration. The program´s goal is to recognize a given protein segment as being a transmembrane domain or non-transmembrane area. The recognizing program to solve this problem will be evolved using the recently developed genetic programming paradigm. Genetic programming starts with a primordial ooze of randomly generated computer programs composed of available programmatic ingredients and then genetically breeds the population using the Darwinian principle of survival of the fittest and the genetic crossover (sexual recombination) operation. Automatic function definition enables genetic programming to dynamically create subroutines (detectors). When cross-validated, the best genetically-evolved recognizer achieves an out-of-sample correlation of 0.968 and an out-of-sample error rate of 1.6%. This error rate is better than that recently reported for five other methods