Prediction of RNA secondary structure by maximizing pseudo-expected accuracy
1 Graduate School of Frontier Sciences, The University of Tokyo,5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8562, Japan
2 Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST),2-4-7 Aomi, Koto-ku, Tokyo 135-0064, Japan
BMC Bioinformatics 2010, 11:586 doi:10.1186/1471-2105-11-586Published: 30 November 2010
Recent studies have revealed the importance of considering the entire distribution of possible secondary structures in RNA secondary structure predictions; therefore, a new type of estimator is proposed including the maximum expected accuracy (MEA) estimator. The MEA-based estimators have been designed to maximize the expected accuracy of the base-pairs and have achieved the highest level of accuracy. Those methods, however, do not give the single best prediction of the structure, but employ parameters to control the trade-off between the sensitivity and the positive predictive value (PPV). It is unclear what parameter value we should use, and even the well-trained default parameter value does not, in general, give the best result in popular accuracy measures to each RNA sequence.
Instead of using the expected values of the popular accuracy measures for RNA secondary structure prediction, which is difficult to be calculated, the pseudo-expected accuracy, which can easily be computed from base-pairing probabilities, is introduced. It is shown that the pseudo-expected accuracy is a good approximation in terms of sensitivity, PPV, MCC, or F-score. The pseudo-expected accuracy can be approximately maximized for each RNA sequence by stochastic sampling. It is also shown that well-balanced secondary structures between sensitivity and PPV can be predicted with a small computational overhead by combining the pseudo-expected accuracy of MCC or F-score with the γ-centroid estimator.
This study gives not only a method for predicting the secondary structure that balances between sensitivity and PPV, but also a general method for approximately maximizing the (pseudo-)expected accuracy with respect to various evaluation measures including MCC and F-score.