Evidence map›Paper›PMID 36699378›Full record

ArticleBioinformatics advances2022

MODalyseR-a novel software for inference of disease module hub regulators identified a putative multiple sclerosis regulator supported by independent eQTL data.

Hendrik A de Weerd, Julia Åkesson, Dimitri Guala, Mika Gustafsson, Zelmina Lubovac-Pilav

Open access · goldAbstract read
In one paragraph

Article in Bioinformatics advances, 2022. 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
0.5field-weighted citation impact, top 40% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. 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

5 authors at 3 institutions in 1 country.

Hendrik A de WeerdSchool of Bioscience, Systems Biology Research Center, University of Skövde, Skövde 541 45, Sweden.ORCID https://orcid.org/0000-0001-7804-1177
Julia ÅkessonSchool of Bioscience, Systems Biology Research Center, University of Skövde, Skövde 541 45, Sweden.ORCID https://orcid.org/0000-0002-9276-0546
Dimitri GualaDepartment of Biochemistry and Biophysics, Stockholm University, Solna 17121, Sweden.ORCID https://orcid.org/0000-0003-2245-7557
Mika GustafssonDepartment of Physics, Chemistry and Biology, Linköping University, Linköping 581 83, Sweden.
Zelmina Lubovac-PilavSchool of Bioscience, Systems Biology Research Center, University of Skövde, Skövde 541 45, Sweden.
Linköping University · SEStockholm University · SEUniversity of Skövde · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Motivation: Network-based disease modules have proven to be a powerful concept for extracting knowledge about disease mechanisms, predicting for example disease risk factors and side effects of treatments. Plenty of tools exist for the purpose of module inference, but less effort has been put on simultaneously utilizing knowledge about regulatory mechanisms for predicting disease module hub regulators. Results: We developed MODalyseR, a novel software for identifying disease module regulators and reducing modules to the most disease-associated genes. This pipeline integrates and extends previously published software packages MODifieR and ComHub and hereby provides a user-friendly network medicine framework combining the concepts of disease modules and hub regulators for precise disease gene identification from transcriptomics data. To demonstrate the usability of the tool, we designed a case study for multiple sclerosis that revealed IKZF1 as a promising hub regulator, which was supported by independent ChIP-seq data. Availability and implementation: MODalyseR is available as a Docker image at https://hub.docker.com/r/ddeweerd/modalyser with user guide and installation instructions found at https://gustafsson-lab.gitlab.io/MODalyseR/. Supplementary information: Supplementary data are available at

Identifiers

PMID36699378
PMCPMC9710626
OpenAlexW4207084874

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.