Insilico Promoter Prediction Using Grey Relational Analysis

In machine learning, multiclass or multi-label classification is the special case within statistical classification of assigning one of several class labels to an input object. The multiclass problem is more complex than binary classification and less researched problem. In biology promoter is the DNA region where the transcription initiation takes place. Reliable recognition of promoter region is essential for understanding biological mechanical of the gene. This study proposes a new approach for predicting the promoter from the DNA sequence based on the modeling of Grey Relational Analysis (GRA). In order to construct a promoter prediction system, GRA approach is developed and applied to the real data set with 2111 samples of promoters and non-promoters of 4 species. The results of the current model are compared to those of traditional ones, logistic regression and back-propagation neural network. The results illustrate that the prediction of the proposed GRA model demonstrates better prediction accuracy than the conventional ones. The current results show that the proposed GRA provides a novel approach in predicting the promoter from a genome.