Abstract
Multiple RNA interaction can be modeled as a problem in combinatorial optimization, where the "optimal" structure is driven by an energy-minimization-like algorithm. However, the actual structure may not be optimal in this computational sense. Moreover, it is not necessarily unique. Therefore, alternative sub-optimal solutions are needed to cover the biological ground. We present a combinatorial formulation for the Multiple RNA Interaction problem with approximation algorithms to handle various interaction patterns, which when combined with Gibbs sampling and MCMC (Markov Chain Monte Carlo), can efficiently generate a reasonable number of optimal and sub-optimal solutions. When viable structures are far from an optimal solution, exploring dependence among different parts of the interaction can increase their score and boost their candidacy for the sampling algorithm. By clustering the solutions, we identify few representatives that are distinct enough to suggest possible alternative structures.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: IEEE/ACM transactions on computational biology and bioinformatics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.