Evidence map›Paper›PMID 39080561›Full record

ArticleBMC bioinformatics2024

Multioviz: an interactive platform for in silico perturbation and interrogation of gene regulatory networks.

Helen Xie, Lorin Crawford, Ashley Mae Conard

Abstract read
In one paragraph

Article in BMC bioinformatics, 2024. 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
–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

1 citing paper in PubMed.

  1. 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

3 authors.

Helen XieDepartment of Computer Science, Brown University, Providence, RI, USA.
Lorin Crawford *Center for Computational Molecular Biology, Brown University, Providence, RI, USA. lcrawford@microsoft.com.ORCID https://orcid.org/0000-0003-0178-8242
Ashley Mae Conard *Department of Computer Science, Brown University, Providence, RI, USA. ashleyconard@microsoft.com.ORCID https://orcid.org/0000-0002-4143-6180

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we aim to build a platform that will help bridge the gap between high-dimensional computation and wet-lab experimentation by allowing users to interrogate genomic signatures at multiple molecular levels and identify best next actionable steps for downstream decision making. We introduce Multioviz: a publicly accessible R package and web application platform to easily perform in silico hypothesis testing of generated gene regulatory networks. We demonstrate the utility of Multioviz by conducting an end-to-end analysis in a statistical genetics application focused on measuring the effect of in silico perturbations of complex trait architecture. By using a real dataset from the Wellcome Trust Centre for Human Genetics, we both recapitulate previous findings and propose hypotheses about the genes involved in the percentage of immune CD8+ cells found in heterogeneous stocks of mice. Source code for the Multioviz R package is available at https://github.com/lcrawlab/multio-viz and an interactive version of the platform is available at https://multioviz.ccv.brown.edu/ .

Indexed as

Gene Regulatory NetworksSoftwareAnimalsComputational BiologyComputer SimulationHumansMice

Identifiers

PMID39080561
PMCPMC11290168

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.