Evidence map›Paper›PMID 33376724›Full record

ArticleBioMed research international2020

Pathway Maps of Orphan and Complex Diseases Using an Integrative Computational Approach.

Kais Ghedira, Soumaya Kouidhi, Yosr Hamdi, Houcemeddine Othman, Sonia Kechaou, Sadri Znaidi, Sghaier Haïtham, Imen Rabhi

Open access · hybridAbstract read
In one paragraph

Article in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 45% 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

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Literature Review on Health Emigration in Rare Diseases-A Machine Learning Perspective.International journal of environmental research and public health · 2023
    Review
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 at 5 institutions in 2 countries.

Kais GhediraLaboratory of Bioinformatics, BioMathematics and Biostatistics (LR16IPT09), Pasteur Institute of Tunisia, University of Tunis El Manar, 1002 Tunis, Tunisia.ORCID https://orcid.org/0000-0002-3387-6493
Soumaya KouidhiLaboratory of Biotechnology and Bio-Geo Resources Valorization (LR11ES31), Higher Institute for Biotechnology, University of Manouba-Biotechpole of Sidi Thabet, 2020, Sidi Thabet, Ariana, Tunisia.
Yosr HamdiLaboratory of Biomedical Genomics and Oncogenetics, LR16IPT05, Pasteur Institute of Tunis, University of Tunis El Manar, Tunis, Tunisia.
Houcemeddine OthmanSydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
Sonia KechaouLaboratory of Bioinformatics, BioMathematics and Biostatistics (LR16IPT09), Pasteur Institute of Tunisia, University of Tunis El Manar, 1002 Tunis, Tunisia.
Sadri ZnaidiLaboratoire de Microbiologie Moléculaire, Vaccinologie et développement Biotechnologique, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia.
Sghaier HaïthamLaboratory of Biotechnology and Bio-Geo Resources Valorization (LR11ES31), Higher Institute for Biotechnology, University of Manouba-Biotechpole of Sidi Thabet, 2020, Sidi Thabet, Ariana, Tunisia.
Imen RabhiLaboratoire de Parasitologie médicale, biotechnologies et biomolécules, Institut Pasteur de Tunis, University of Tunis El Manar, Tunis, Tunisia.
Tunis University · TNInstitut Pasteur de Tunis · TNCentre National des Sciences et Technologies Nucléaires · TNManouba University · TNUniversity of the Witwatersrand · ZA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Orphan diseases (ODs) are progressive genetic disorders, which affect a small number of people. The principal fundamental aspects related to these diseases include insufficient knowledge of mechanisms involved in the physiopathology necessary to access correct diagnosis and to develop appropriate healthcare. Unlike ODs, complex diseases (CDs) have been widely studied due to their high incidence and prevalence allowing to understand the underlying mechanisms controlling their physiopathology. Few studies have focused on the relationship between ODs and CDs to identify potential shared pathways and related molecular mechanisms which would allow improving disease diagnosis, prognosis, and treatment. We have performed a computational approach to studying CDs and ODs relationships through (1) connecting diseases to genes based on genes-diseases associations from public databases, (2) connecting ODs and CDs through binary associations based on common associated genes, and (3) linking ODs and CDs to common enriched pathways. Among the most shared significant pathways between ODs and CDs, we found pathways in cancer, p53 signaling, mismatch repair, mTOR signaling, B cell receptor signaling, and apoptosis pathways. Our findings represent a reliable resource that will contribute to identify the relationships between drugs and disease-pathway networks, enabling to optimise patient diagnosis and disease treatment.

Indexed as

Databases, GeneticGene Regulatory NetworksProtein Interaction MapsAlgorithmsAnimalsApoptosisComputational BiologyComputer SimulationHumansMutationNeoplasmsRare DiseasesSignal Transduction

Identifiers

PMID33376724
PMCPMC7744584
OpenAlexW3106550583

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.