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KMID : 1132720200180010002
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Genomics & Informatics
2020 Volume.18 No. 1 p.2 ~ p.2
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Detecting outliers in segmented genomes of flu virus using an alignment-free approach
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Daoud Mosaab
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Abstract
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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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KEYWORD
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composite data point, distance space, flu virus, Mosaab-metric space, outliers, statistical learning
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