Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A minimum spanning tree algorithm with inverse-Ackermann type complexity
Journal of the ACM (JACM)
Bioinformatics
A grammatical approach to RNA-RNA interaction prediction
Pattern Recognition
Bioinformatics
On the Approximation of Optimal Structures for RNA-RNA Interaction
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Time and space efficient RNA-RNA interaction prediction via sparse folding
RECOMB'10 Proceedings of the 14th Annual international conference on Research in Computational Molecular Biology
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We present biRNA, a novel algorithm for prediction of binding sites between two RNAs based on minimization of binding free energy. Similar to RNAup approach [30], we assume the binding free energy is the sum of accessibility and the interaction free energies. Our algorithm maintains tractability and speed and also has two important advantages over previous similar approaches: (1) biRNA is able to predict multiple simultaneous binding sites and (2) it computes a more accurate interaction free energy by considering both intramolecular and intermolecular base pairing. Moreover, biRNA can handle crossing interactions as well as hairpins interacting in a zigzag fashion. To deal with simultaneous accessibility of binding sites, our algorithm models their joint probability of being unpaired. Since computing the exact joint probability distribution is intractable, we approximate the joint probability by a polynomially representable graphical model namely a Chow-Liu tree-structured Markov Random Field. Experimental results show that biRNA outperforms RNAup and also support the accuracy of our approach. Our proposed Bayesian approximation of the Boltzmann joint probability distribution provides a powerful, novel framework that can also be utilized in other applications.