Integrating genomic binding site predictions using real-valued meta classifiers (original) (raw)

2008, Neural Computing and Applications

Currently the best algorithms for predicting transcription factor binding sites in DNA sequences are severely limited in accuracy. There is good reason to believe that predictions from different classes of algorithms could be used in conjunction to improve the quality of predictions. In this paper, we apply Single Layer Networks, Rules Sets, Support Vector Machines and the Adaboost algorithm to predictions from 12 key real valued algorithms. Furthermore, we use a 'window' of consecutive results as the input vector in order to contextualise the neighbouring results. We improve the classification result with the aid of under-and over-sampling techniques. We find that Support Vector Machines and the Adaboost algorithm outperform the original individual algorithms and the other classifiers employed in this work. In particular they give a better tradeoff between Recall and Precision.

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