Evidence map›Paper›PMID 30864351›Full record

ArticleJournal of integrative bioinformatics2018

Search for New Candidate Genes Involved in the Comorbidity of Asthma and Hypertension Based on Automatic Analysis of Scientific Literature.

Olga V Saik, Pavel S Demenkov, Timofey V Ivanisenko, Elena Yu Bragina, Maxim B Freidin, Victor E Dosenko, Olga I Zolotareva, Evgeniy L Choynzonov, Ralf Hofestaedt, Vladimir A Ivanisenko

Open access · diamondAbstract read
In one paragraph

Article in Journal of integrative bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.7field-weighted citation impact, top 23% of its field
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

7 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
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

10 authors at 5 institutions in 3 countries.

Olga V SaikInstitute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia.
Pavel S DemenkovInstitute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia.
Timofey V IvanisenkoInstitute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia.
Elena Yu BraginaResearch Institute of Medical Genetics, Tomsk NRMC, Tomsk, Russia.ORCID https://orcid.org/0000-0002-1103-3073
Maxim B FreidinResearch Institute of Medical Genetics, Tomsk NRMC, Tomsk, Russia.
Victor E DosenkoBogomoletz Institute of Physiology, National Academy of Science, Kiev, Ukraine.
Olga I ZolotarevaBielefeld University, International Research Training Group "Computational Methods for the Analysis of the Diversity and Dynamics of Genomes", Bielefeld, Germany.
Evgeniy L ChoynzonovTomsk National Research Medical Center, Russian Academy of Sciences, Tomsk, Russia.
Ralf HofestaedtBielefeld University, Technical Faculty, AG Bioinformatics and Medical Informatics, Bielefeld, Germany.
Vladimir A IvanisenkoInstitute of Cytology and Genetics, Siberian Branch, Russian Academy of Sciences, Novosibirsk, Russia.
Institute of Cytology and Genetics · RUBielefeld University · DEResearch Institute of Medical Genetics of Russian Academy of Medical Sciences · RUBogomoletz Institute of Physiology · UARussian Academy of Sciences · RU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Comorbid states of diseases significantly complicate diagnosis and treatment. Molecular mechanisms of comorbid states of asthma and hypertension are still poorly understood. Prioritization is a way for identifying genes involved in complex phenotypic traits. Existing methods of prioritization consider genetic, expression and evolutionary data, molecular-genetic networks and other. In the case of molecular-genetic networks, as a rule, protein-protein interactions and KEGG networks are used. ANDSystem allows reconstructing associative gene networks, which include more than 20 types of interactions, including protein-protein interactions, expression regulation, transport, catalysis, etc. In this work, a set of genes has been prioritized to find genes potentially involved in asthma and hypertension comorbidity. The prioritization was carried out using well-known methods (ToppGene and Endeavor) and a cross-talk centrality criterion, calculated by analysis of associative gene networks from ANDSystem. The identified genes, including IL1A, CD40LG, STAT3, IL15, FAS, APP, TLR2, C3, IL13 and CXCL10, may be involved in the molecular mechanisms of comorbid asthma/hypertension. An analysis of the dynamics of the frequency of mentioning the most priority genes in scientific publications revealed that the top 100 priority genes are significantly enriched with genes with increased positive dynamics, which may be a positive sign for further studies of these genes.

Indexed as

Gene Regulatory NetworksAsthmaBiomarkersComorbidityComputational BiologyData MiningGermanyHumansHypertensionSoftwareBiomarkersANDSystemassociative gene networkasthmacomorbid diseasedynamics of interest of genes in publicationsgene prioritizationhypertension

Identifiers

PMID30864351
PMCPMC6348743
OpenAlexW2909014396

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.