Evidence map›Paper›PMID 34630741›Full record

ArticleProcedia computer science2021

OntoRepliCov: an Ontology-Based Approach for Modeling the SARS-CoV-2 Replication Process.

Wissame Laddada, Lina F Soualmia, Cecilia Zanni-Merk, Ali Ayadi, Claudia Frydman, India L'Hote, Isabelle Imbert

Abstract read
In one paragraph

Article in Procedia computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

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4 · The record

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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

7 authors.

Wissame LaddadaNormandie Universit, LITIS, 7600 Rouen, France.
Lina F SoualmiaNormandie Universit, LITIS, 7600 Rouen, France.
Cecilia Zanni-MerkNormandie Universit, LITIS, 7600 Rouen, France.
Ali AyadiAix-Marseille Universit, LIS, 13009 Marseille, France.
Claudia FrydmanAix-Marseille Universit, LIS, 13009 Marseille, France.
India L'HoteAix-Marseille Universit, AFMB, 13009 Marseille, France.
Isabelle ImbertAix-Marseille Universit, AFMB, 13009 Marseille, France.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the replication machinery of viruses contributes to suggest and try effective antiviral strategies. Exhaustive knowledge about the proteins structure, their function, or their interaction is one of the preconditions for successfully modeling it. In this context, modeling methods based on a formal representation with a high semantic expressiveness would be relevant to extract proteins and their nucleotide or amino acid sequences as an element from the replication process. Consequently, our approach relies on the use of semantic technologies to design the SARS-CoV-2 replication machinery. This provides the ability to infer new knowledge related to each step of the virus replication. More specifically, we developed an ontology-based approach enriched with reasoning process of a complete replication machinery process for SARS-CoV-2. We present in this paper a partial overview of our ontology OntoRepliCov to describe one step of this process, namely, the continuous translation or protein synthesis, through classes, properties, axioms, and SWRL (Semantic Web Rule Language) rules.

Indexed as

biology domainKnowledge representationreasoning processSWRL

Identifiers

PMID34630741
PMCPMC8486259

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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.