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Open Quantum Systems Decoherence Quantum Machine Learning

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

DOAJ
Authors: Mosaab Daoud

Year

2020

Paper ID

4632

Status

Peer-reviewed

Abstract Read

~2 min

Abstract Words

96

Citations

3

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.

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  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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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.

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References & Citation Signals

[1] DOI https://doi.org/10.5808/GI.2020.18.1.e2 [2] Publisher http://genominfo.org/upload/pdf/gi-2020-18-1-e2.pdf

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External citation index: OpenAlex citation signal • updated 2026-10-22 16:20:22

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