Evidence map›Paper›PMID 30500839›Full record

ArticlePloS one2018

Agronomic Linked Data (AgroLD): A knowledge-based system to enable integrative biology in agronomy.

Aravind Venkatesan, Gildas Tagny Ngompe, Nordine El Hassouni, Imene Chentli, Valentin Guignon, Clement Jonquet, Manuel Ruiz, Pierre Larmande

Abstract read
In one paragraph

Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

8 authors.

Aravind VenkatesanInstitut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France.
Gildas Tagny NgompeInstitut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France.
Nordine El HassouniInstitut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France.
Imene ChentliInstitut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France.
Valentin GuignonSouth Green Bioinformatics Platform, Montpellier, France.
Clement JonquetInstitut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France.
Manuel RuizInstitut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France.
Pierre LarmandeInstitut de Biologie Computationnelle (IBC), Univ. of Montpellier, Montpellier, France.ORCID 0000-0002-2923-9790

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advances in high-throughput technologies have resulted in a tremendous increase in the amount of omics data produced in plant science. This increase, in conjunction with the heterogeneity and variability of the data, presents a major challenge to adopt an integrative research approach. We are facing an urgent need to effectively integrate and assimilate complementary datasets to understand the biological system as a whole. The Semantic Web offers technologies for the integration of heterogeneous data and their transformation into explicit knowledge thanks to ontologies. We have developed the Agronomic Linked Data (AgroLD- www.agrold.org), a knowledge-based system relying on Semantic Web technologies and exploiting standard domain ontologies, to integrate data about plant species of high interest for the plant science community e.g., rice, wheat, arabidopsis. We present some integration results of the project, which initially focused on genomics, proteomics and phenomics. AgroLD is now an RDF (Resource Description Format) knowledge base of 100M triples created by annotating and integrating more than 50 datasets coming from 10 data sources-such as Gramene.org and TropGeneDB-with 10 ontologies-such as the Gene Ontology and Plant Trait Ontology. Our evaluation results show users appreciate the multiple query modes which support different use cases. AgroLD's objective is to offer a domain specific knowledge platform to solve complex biological and agronomical questions related to the implication of genes/proteins in, for instances, plant disease resistance or high yield traits. We expect the resolution of these questions to facilitate the formulation of new scientific hypotheses to be validated with a knowledge-oriented approach.

Indexed as

AgricultureGenomicsKnowledge BasesProteomicsGenome, Plant

Identifiers

PMID30500839
PMCPMC6269127

What OpenQuestion holds

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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.