Evidence map›Paper›PMID 32901204›Full record

ArticleApplied soft computing2020

Analyzing COVID-19 virus based on enhanced fragmented biological Local Aligner using improved Ions Motion Optimization algorithm.

Mohamed Issa, Mohamed Abd Elaziz

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Applied soft computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
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  5. A Review of Parallel Implementations for the Smith-Waterman Algorithm.Interdisciplinary sciences, computational life sciences · 2022
    Review
  6. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Mohamed IssaComputer and Systems Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt.
Mohamed Abd ElazizHubei Engineering Research Center on Big Data Security, School of Cyber Science & Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

SARS-CoV-2 (COVID-19) virus is a havoc pandemic that infects millions of people over the world and thousands of infected cases dead. So, it is vital to propose new intelligent data analysis tools and enhance the existed ones to aid scientists in analyzing the COVID-19 virus. Fragmented Local Aligner Technique (FLAT) is a data analysis tool that is used for detecting the longest common consecutive subsequence (LCCS) between a pair of biological data sequences. FLAT is an aligner tool that can be used to find the LCCS between COVID-19 virus and other viruses to help in other biochemistry and biological operations. In this study, the enhancement of FLAT based on modified Ions Motion Optimization (IMO) is developed to produce acceptable LCCS with efficient performance in a reasonable time. The proposed method was tested to find the LCCS between Orflab poly-protein and surface glycoprotein of COVID-19 and other viruses. The experimental results demonstrate that the proposed model succeeded in producing the best LCCS against other algorithms using real LCCS measured by the SW algorithm as a reference.

Indexed as

COVID-19 virusIons motion optimizationLongest common consecutive substringsPairwise local alignmentSmith–Waterman alignment algorithm

Identifiers

PMID32901204
PMCPMC7467904

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.