Detecting outliers in segmented genomes of flu virus using an alignment-free approach
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 mea...
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BioMed Central
2020-03-01
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Series: | Genomics & Informatics |
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Online Access: | http://genominfo.org/upload/pdf/gi-2020-18-1-e2.pdf |
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author | Mosaab Daoud |
author_facet | Mosaab Daoud |
author_sort | Mosaab Daoud |
collection | DOAJ |
description | 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. |
format | Article |
id | doaj-art-003833ba983a47c5b19a9f68e2d4db0e |
institution | Kabale University |
issn | 2234-0742 |
language | English |
publishDate | 2020-03-01 |
publisher | BioMed Central |
record_format | Article |
series | Genomics & Informatics |
spelling | doaj-art-003833ba983a47c5b19a9f68e2d4db0e2025-02-02T07:02:37ZengBioMed CentralGenomics & Informatics2234-07422020-03-0118110.5808/GI.2020.18.1.e2595Detecting outliers in segmented genomes of flu virus using an alignment-free approachMosaab DaoudIn 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.http://genominfo.org/upload/pdf/gi-2020-18-1-e2.pdfcomposite data pointdistance spaceflu virusmosaab-metric spaceoutliersstatistical learning |
spellingShingle | Mosaab Daoud Detecting outliers in segmented genomes of flu virus using an alignment-free approach Genomics & Informatics composite data point distance space flu virus mosaab-metric space outliers statistical learning |
title | Detecting outliers in segmented genomes of flu virus using an alignment-free approach |
title_full | Detecting outliers in segmented genomes of flu virus using an alignment-free approach |
title_fullStr | Detecting outliers in segmented genomes of flu virus using an alignment-free approach |
title_full_unstemmed | Detecting outliers in segmented genomes of flu virus using an alignment-free approach |
title_short | Detecting outliers in segmented genomes of flu virus using an alignment-free approach |
title_sort | detecting outliers in segmented genomes of flu virus using an alignment free approach |
topic | composite data point distance space flu virus mosaab-metric space outliers statistical learning |
url | http://genominfo.org/upload/pdf/gi-2020-18-1-e2.pdf |
work_keys_str_mv | AT mosaabdaoud detectingoutliersinsegmentedgenomesoffluvirususinganalignmentfreeapproach |