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Journal of Reliable Intelligent Environments

, Volume 5, Issue 4, pp 241–257 | Cite as

Nature inspired optimization algorithm for prediction of “minimum free energy” “RNA secondary structure”

  • Ashish Tripathi
  • K. K. Mishra
  • Shailesh TiwariEmail author
  • P. C. Vashist
Original Article
  • 69 Downloads

Abstract

Over the last few years, many optimization algorithms have been developed to predict the optimal secondary structure of ribonucleic acid (RNA) with “minimum free energy” (MFE). These algorithms are either inspired by dynamic programming or by meta-heuristic techniques. RNA participates in several biological activities in the organism. These activities involve protein synthesis, understanding the functional behavior of RNA molecules, coding, decoding and gene expression, carrier of transferring genetic information, formation of protein, catalyst in biomedical reactions and structural molecule in cellular organelles, transcription, etc. Beside the said activities, the major role of RNA is in developing new drugs and understanding several diseases occurred due to genetic disorder and viruses. For the above said activities, it is required to predict the correct RNA secondary structure having minimum free energy with desired prediction accuracy. This paper presents a meta-heuristic optimization algorithm to obtain the optimal secondary structure of RNA with required functionality and requires less time than the others in the literature. The performance of the proposed algorithm is checked with different existing state-of-the-art techniques. It is found that the proposed algorithm gives better results against the other techniques.

Keywords

Optimization EAMD “Minimum free energy” Adaptive learning Pseudoknots 

Notes

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of ITG. L. Bajaj Institute of Technology & ManagementGreater NoidaIndia
  2. 2.CSEDMNNITAllahabadIndia
  3. 3.CSEDABES Engineering CollegeGhaziabadIndia

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