Is it better to combine predictions?

Ross D. King, Mohammed Ouali, Arbra T. Strong, Alaaeldin Aly, Adel Elmaghraby, Mehmed Kantardzic, David Page

    Research output: Contribution to journalArticlepeer-review

    36 Citations (Scopus)


    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 nonhomologous 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.

    Original languageEnglish
    Pages (from-to)15-19
    Number of pages5
    JournalProtein Engineering
    Issue number1
    Publication statusPublished - 2000


    • Machine-learning
    • Neural-networks
    • Secondary structure
    • Statistics

    ASJC Scopus subject areas

    • Biochemistry
    • Molecular Biology


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