Protein Engineering, Vol. 13, No. 1, 15-19,
January 2000
© 2000 Oxford University Press
Is it better to combine predictions?
Department of Computer Science, University of Wales, Aberystwyth Penglais, Aberystwyth, Ceredigion, SY23 3DB, Wales, UK, 2 Department of Engineering Mathematics and Computer Science, Speed Scientific School, University of Louisville, Louisville, KY 40292, USA and 3 Department of Biostatistics and Medical Informatics and Department of Computer Sciences, University of Wisconsin, 1300 University Avenue, Room 5795 Medical Sciences, Madison, WI 53706, USA.Email: page{at}biostac.wisc.edu
We have compared the accuracy of the individual protein secondary structure prediction methods: PHD, DSC, NNSSP and Predator against the accuracy obtained by combing the predictions of the methods. A range of ways of combing predictions were tested: voting, biased voting, linear discrimination, neural networks and decision trees. The combined methods that involve `learning' (the non-voting methods) were trained using a set of 496 non-homologous domains; this dataset was biased as some of the secondary structure prediction methods had used them for training. We used two independent test sets to compare predictions: the first consisted of 17 non-homologous domains from CASP3 (Third Community Wide Experiment on the Critical Assessment of Techniques for Protein Structure Prediction); the second set consisted of 405 domains that were selected in the same way as the training set, and were non-homologous to each other and the training set. On both test datasets the most accurate individual method was NNSSP, then PHD, DSC and the least accurate was Predator; however, it was not possible to conclusively show a significant difference between the individual methods. Comparing the accuracy of the single methods with that obtained by combing predictions it was found that it was better to use a combination of predictions. On both test datasets it was possible to obtain a ~3% improvement in accuracy by combing predictions. In most cases the combined methods were statistically significantly better (at P = 0.05 on the CASP3 test set, and P = 0.01 on the EBI test set). On the CASP3 test dataset there was no significant difference in accuracy between any of the combined method of prediction: on the EBI test dataset, linear discrimination and neural networks significantly outperformed voting techniques. We conclude that it is better to combine predictions.
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