Combing ontologies and dipeptide composition for predicting DNA-binding proteins

Given a novel protein it is very important to know if it is a DNA-binding protein, because DNA-binding proteins participate in the fundamental role to regulate gene expression. In this work, we propose a parallel fusion between a classifier trained using the features extracted from the gene ontology database and a classifier trained using the dipeptide composition of the protein. As classifiers the support vector machine (SVM) and the 1-nearest neighbour are used. Matthews's correlation coefficient obtained by our fusion method is approximate to 0.97 when the jackknife cross-validation is used; this result outperforms the best performance obtained in the literature (0.924) using the same dataset where the SVM is trained using only the Chou's pseudo amino acid based features. In this work also the area under the ROC-curve (AUC) is reported and our results show that the fusion permits to obtain a very interesting 0.995 AUC. In particular we want to stress that our fusion obtains a 5% false negative with a 0% of false positive. Matthews's correlation coefficient obtained using the single best GO-number is only 0.7211 and hence it is not possible to use the gene ontology database as a simple lookup table. Finally, we test the complementarity of the two tested feature extraction methods using the Q-statistic. We obtain the very interesting result of 0.58, which means that the features extracted from the gene ontology database and the features extracted from the amino acid sequence are partially independent and that their parallel fusion should be studied more.