Detecting outliers in segmented genomes of flu virus using an alignment-free approach
- Author:
Mosaab DAOUD
1
Author Information
- Publication Type:Original article
- From:Genomics & Informatics 2020;18(1):e2-
- CountryRepublic of Korea
- Language:English
- 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.
