Evolutionary neural logic networks in splice-junction gene sequences classification
The paper demonstrates the efficient use of hybrid intelligent systems for solving the classification problem of splice-junction gene sequences. The aim of the study is to obtain classification schemes able to recognize, given a sequence of DNA, the boundaries between exons and introns. Previous attempts to form efficient classifiers for the same problem using intelligent or standard statistical techniques are discussed throughout the paper. The authors propose the use of evolutionary neural logic networks, an advantageous approach for their ability to interpret their structure into expert rules, a desirable feature for field experts. Evolutionary neural logic networks in fact consist an innovative hybrid intelligent methodology, by which evolutionary programming techniques are used for obtaining the best possible topology of a neural logic network. The genetic programming process is guided using a context-free grammar and indirect encoding of the neural logic networks into the genetic programming individuals. Indicative classification results are presented and discussed in detail in terms of both, classification accuracy and solution interpretability.
neural logic networks splicing eukaryotic dna grammar-guided genetic programming cellular encoding