iEnhancer-MRBF: Identifying enhancers and their strength with a multiple Laplacian-regularized radial basis function network.

An enhancer is a short DNA sequence containing many binding sites of transcription factors that plays a crucial role in the gene expression of major eukaryotes. It is difficult to avoid the time consumption and high cost of experimental methods. Therefore, with the continuous development of genomics, it is an urgent task to identify enhancers and their intensities by computational methods. In this paper, we propose a two-layer model called iEnhancer-MRBF, wherein the first layer is used to identify enhancers, and the identified enhancers are divided into strong enhancers and weak enhancers according to their strength in the second layer. In iEnhancer-MRBF, a new classifier multiple Laplacian-regularized radial basis function network (MLR-RBFN) is proposed, and three feature representation methods, namely, kmer, nucleotide binary profiles (NBP) and ac-cumulated nucleotide frequency (ANF), as well as feature selection, are used to process DNA sequences. The experimental results show that the model is significantly better than the previous prediction models, and the test accuracy rates of the first and second layers of independent datasets are 79.75% and 83.50%, respectively.