Genomics Inform.  2020 Mar;18(1):e2. 10.5808/GI.2020.18.1.e2.

Detecting outliers in segmented genomes of flu virus using an alignment-free approach

• Daoud M

• KMID: 2501322
• DOI: http://doi.org/10.5808/GI.2020.18.1.e2

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.

Keyword

composite data point; flu virus; outliers; distance space; statistical learning; Mosaab-metric space