Dempster-Shafer Theory for the Prediction of Auxin-Response Elements (AuxREs) in Plant Genomes

Abstract

Auxin is a major regulator of plant growth and development; its action involves transcriptional activation. The identification of Auxin-response element (AuxRE) is one of the most important issues to understand the Auxin regulation of gene expression. Over the past few years, a large number of motif identification tools have been developed. Despite these considerable efforts provided by computational biologists, building reliable models to predict regulatory elements has still been a difficult challenge. In this context, we propose in this work a data fusion approach for the prediction of AuxRE. Our method is based on the combined use of Dempster-Shafer evidence theory and fuzzy theory. To evaluate our model, we have scanning the DORNRÖSCHEN promoter by our model. All proven AuxRE present in the promoter has been detected. At the 0.9 threshold we have no false positive. The comparison of the results of our model and some previous motifs finding tools shows that our model can predict AuxRE more successfully than the other tools and produce less false positive. The comparison of the results before and after combination shows the importance of Dempster-Shafer combination in the decrease of false positive and to improve the reliability of prediction. For an overall evaluation we have chosen to present the performance of our approach in comparison with other methods. In fact, the results indicated that the data fusion method has the highest degree of sensitivity (Sn) and Positive Predictive Value (PPV).