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• W2910742847 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." @default.
• W2910742847 created "2019-01-25" @default.
• W2910742847 creator A5070998275 @default.
• W2910742847 creator A5078748432 @default.
• W2910742847 date "2019-05-01" @default.
• W2910742847 modified "2023-10-18" @default.
• W2910742847 title "Gibbs/MCMC Sampling for Multiple RNA Interaction with Sub-Optimal Solutions" @default.
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• W2910742847 doi "https://doi.org/10.1109/tcbb.2018.2890519" @default.
• W2910742847 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30629511" @default.
• W2910742847 hasPublicationYear "2019" @default.
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