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Maximising Overlap Score in DNA Sequence Assembly Problem by Stochastic Diffusion Search

  • Fatimah Majid al-RifaieEmail author
  • Mohammad Majid al-Rifaie
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 650)

Abstract

This paper introduces a novel study on the performance of Stochastic Diffusion Search (SDS)—a swarm intelligence algorithm—to address DNA sequence assembly problem. This is an NP-hard problem and one of the primary problems in computational molecular biology that requires optimisation methodologies to reconstruct the original DNA sequence. In this work, SDS algorithm is adapted for this purpose and several experiments are run in order to evaluate the performance of the presented technique over several frequently used benchmarks. Given the promising results of the newly proposed algorithm and its success in assembling the input fragments, its behaviour is further analysed, thus shedding light on the process through which the algorithm conducts the task. Additionally, the algorithm is applied to overlap score matrices which are generated from the raw input fragments; the algorithm optimises the overlap score matrices to find better results. In these experiments real-world data are used and the performance of SDS is compared with several other algorithms which are used by other researchers in the field, thus demonstrating its weaknesses and strengths in the experiments presented in the paper.

Keywords

Particle Swarm Optimisation Swarm Intelligence Assembly Problem Swarm Intelligence Algorithm Unknown Orientation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Fatimah Majid al-Rifaie
    • 1
    Email author
  • Mohammad Majid al-Rifaie
    • 2
  1. 1.Department of Computer ScienceKristianstad UniversityKristianstadSweden
  2. 2.Computing DepartmentGoldsmiths, University of LondonLondonUK

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