Genomics & Informatics

Korea Genome Organization (한국유전체학회)
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Detecting outliers in segmented genomes of flu virus using an alignment-free approach

  • Received : 2019.10.25
  • Accepted : 2019.11.21
  • Published : 2020.03.31
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Abstract

In this paper, we propose a new approach to detecting outliers in a set of segmented genomes of the flu virus, a data set with a heterogeneous set of sequences. The approach has the following computational phases: feature extraction, which is a mapping into feature space, alignment-free distance measure to measure the distance between any two segmented genomes, and a mapping into distance space to analyze a quantum of distance values. The approach is implemented using supervised and unsupervised learning modes. The experiments show robustness in detecting outliers of the segmented genome of the flu virus.

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References

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