Employing Genetic Algorithm to Construct Epigenetic Tree-Based Features for Enhancer Region Prediction

This paper presents a GA-based method to generate novel logical-based features, represented by parse trees, from DNA sequences enriched with H3K4me1 histone signatures. Current methods which mostly utilize k-mers content features are not able to represent the possible complex interaction of various DNA segments in H3K4me1 regions. We hypothesize that such complex interaction modeling is significant towards recognition of H3K4me1 marks. Our propose method employ the tree structure to model the logical relationship between k-mers from the marks. To benchmark our generated features, we compare it to the typically used k-mer content features using the mouse (mm9) genome dataset. Our results show that the logical rule features improve the performance in terms of f-measure for all the datasets tested