Evidence map›Paper›PMID 34746856›Full record

ArticleSTAR protocols2021

Computational workflow for functional characterization of COVID-19 through secondary data analysis.

Sudhir Ghandikota, Mihika Sharma, Anil G Jegga

Abstract read
In one paragraph

Article in STAR protocols, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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.

Sudhir GhandikotaDivision of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Mihika SharmaDivision of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
Anil G JeggaDivision of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.

Funding

Preclinical characterization of saracatinib for cystic fibrosis therapyUG3TR002612 · NCATS · CINCINNATI CHILDRENS HOSP MED CTR · PI CLANCY, JOHN PAUL, JEGGA, ANIL · 2018 to 2019
$636k
NCATS NIH HHS UG3 TR002612
6 · The paper itself

Abstract

Standard transcriptomic analyses cannot fully capture the molecular mechanisms underlying disease pathophysiology and outcomes. We present a computational heterogeneous data integration and mining protocol that combines transcriptional signatures from multiple model systems, protein-protein interactions, single-cell RNA-seq markers, and phenotype-genotype associations to identify functional feature complexes. These feature modules represent a higher order multifeatured machines collectively working toward common pathophysiological goals. We apply this protocol for functional characterization of COVID-19, but it could be applied to many other diseases. For complete details on the use and execution of this protocol, please refer to Ghandikota et al. (2021).

Indexed as

Data AnalysisSoftwareTranscriptomeWorkflowComputational BiologyCOVID-19HumansSARS-CoV-2BioinformaticsGene ExpressionGenomicsHealth SciencesImmunologyRNAseqSingle CellSystems biology

Identifiers

PMID34746856
PMCPMC8551262

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.