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Making Multiple RNA Interaction Practical

  • Syed Ali Ahmed
  • Saman Farhat
  • Saad Mneimneh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11346)

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 extend a recent combinatorial formulation for the Multiple RNA Interaction problem with approximation algorithms to handle more elaborate 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.

Keywords

Multiple RNA interaction NP-hardness Approximation algorithms Gibbs sampling MCMC Clustering 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Syed Ali Ahmed
    • 1
    • 3
  • Saman Farhat
    • 1
  • Saad Mneimneh
    • 1
    • 2
  1. 1.The Graduate CenterCity University of New YorkNew YorkUSA
  2. 2.Hunter CollegeCity University of New YorkNew YorkUSA
  3. 3.Google LLCMountain ViewUSA

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