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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| Format: | Article |
| Language: | English |
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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 |