Asai Laboratory

In this work, we constructed a general framework for designing estimators for estimation problems in high-dimensional discrete (binary) spaces. The theory is regarded as a generalization of the pioneering work conducted by Carvalho and Lawrence, and is closely related to the concept of MEA. Furthermore, we presented several applications of the proposed estimators and the underlying theory. The concept presented in this paper is highly extendable and sheds new light on many problems in bioinformatics.

感度と精度のバランスの良いＲＮＡ2次構造予測を行う手法です。
BMC Bioinformatics 2010, 11:586
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 g-centroid estimator.

Michiaki Hamada, Kengo Sato　and Kiyoshi Asai, Improving the accuracy of predicting secondary structure for aligned RNA sequences, Nucl. Acids Res. (2010) doi: 10.1093/nar/gkq792 First published online: September 15, 2010

Kato Y, Sato K, Hamada M, Watanabe Y, Asai K, Akutsu T.
RactIP: fast and accurate prediction of RNA-RNA interaction using integer programming,
Bioinformatics Vol. 26 ECCB 2010, pages i460–i466.
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