Using pre and post-processing methods to improve binding site predictions

Currently the best algorithms for transcription factor binding site prediction within sequences of regulatory DNA are severely limited in accuracy. In this paper, we integrate 12 original binding site prediction algorithms, and use a `window' of consecutive predictions in order to contextualise the neighbouring results. We combine either random selection or Tomek links under-sampling with SMOTE over-sampling techniques. In addition, we investigate the behaviour of four feature selection filtering methods: Bi-Normal Separation, Correlation Coefficients, F-Score and a cross entropy based algorithm. Finally, we remove some of the final predicted binding sites on the basis of their biological plausibility. The results show that we can generate a new prediction that significantly improves on the performance of any one of the individual algorithms.