Enhancer-DSNet: A Supervisedly Prepared Enriched Sequence Representation for the Identification of Enhancers and Their Strength

Identification of enhancers and their strength prediction plays an important role in gene expression regulation and currently an active area of research. However, its identification specifically through experimental approaches is extremely time consuming and labor-intensive task. Several machine learning methodologies have been proposed to accurately discriminate enhancers from regulatory elements and to estimate their strength. Existing approaches utilise different statistical measures for feature encoding which mainly capture residue specific physico-chemical properties upto certain extent but ignore semantic and positional information of residues. This paper presents "Enhancer-DSNet", a two-layer precisely deep neural network which makes use of a novel k-mer based sequence representation scheme prepared by fusing associations between k-mer positions and sequence type. Proposed Enhancer-DSNet methodology is evaluated on a publicly available benchmark dataset and independent test set. Experimental results over benchmark independent test set indicate that proposed Enhancer-DSNet methodology outshines the performance of most recent predictor by the figure of 2%, 1%, 2%, and 5% in terms of accuracy, specificity, sensitivity and matthews correlation coefficient for enhancer identification task and by the figure of 15%, 21%, and 39% in terms of accuracy, specificity, and matthews correlation coefficient for strong/weak enhancer prediction task.