Evidence map›Paper›PMID 33490923›Full record

ArticleiScience2021

Prioritizing transcriptional factors in gene regulatory networks with PageRank.

Hongxu Ding, Ying Yang, Yuanqing Xue, Lucas Seninge, Henry Gong, Rojin Safavi, Andrea Califano, Joshua M Stuart

Abstract read
In one paragraph

Article in iScience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Article
  3. The α-synuclein proteostasis network and its translational applications in Parkinson's disease.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. 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

8 authors.

Hongxu DingDepartment of Biomolecular Engineering and Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA.
Ying YangDepartment of Biomolecular Engineering and Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA.
Yuanqing XueDepartment of Biomolecular Engineering and Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA.
Lucas SeningeDepartment of Biomolecular Engineering and Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA.
Henry GongDepartment of Biomolecular Engineering and Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA.
Rojin SafaviDepartment of Biomolecular Engineering and Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA.
Andrea CalifanoDepartment of Systems Biology, Columbia University, New York, NY, USA.
Joshua M StuartDepartment of Biomolecular Engineering and Genomics Institute, University of California, Santa Cruz, Santa Cruz, CA, USA.

Funding

New Integrative Pathway Analysis Methods to Predict Biomedical OutcomesR01GM109031 · NIGMS · UNIVERSITY OF CALIFORNIA SANTA CRUZ · PI STUART, JOSHUA MICHAEL · 2014 to 2018
$2.9M
NIGMS NIH HHS R01 GM109031
6 · The paper itself

Abstract

Biological states are controlled by orchestrated transcriptional factors (TFs) within gene regulatory networks. Here we show TFs responsible for the dynamic changes of biological states can be prioritized with temporal PageRank. We further show such TF prioritization can be extended by integrating gene regulatory networks reverse engineered from multi-omics profiles, e.g. gene expression, chromatin accessibility, and chromosome conformation assays, using multiplex PageRank.

Indexed as

Gene NetworkMolecular BiologyOmics

Identifiers

PMID33490923
PMCPMC7809505

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.