Evidence map›Paper›PMID 30266789›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2018

Literature-based automated discovery of tumor suppressor p53 phosphorylation and inhibition by NEK2.

Byung-Kwon Choi, Tajhal Dayaram, Neha Parikh, Angela D Wilkins, Meena Nagarajan, Ilya B Novikov, Benjamin J Bachman, Sung Yun Jung, Peter J Haas, Jacques L Labrie and 15 more

Open access · bronzeAbstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed
2.2field-weighted citation impact, top 11% 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

30 citing papers in PubMed, 54 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Role of NEK2 in tumorigenesis and tumor progression.Trends in molecular medicine · 2025
    Review
  6. Article
  7. The emerging role of Never-in-Mitosis A - Related Kinases in the endothelium.The international journal of biochemistry & cell biology · 2024
    Review
  8. Article
  9. Article
  10. Review
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. 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

25 authors at 3 institutions in 1 country.

Byung-Kwon ChoiDepartment of Human and Molecular Genetics, Baylor College of Medicine, Houston, TX 77030.
Tajhal DayaramDepartment of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX 77030.
Neha ParikhDepartment of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX 77030.
Angela D WilkinsDepartment of Human and Molecular Genetics, Baylor College of Medicine, Houston, TX 77030.
Meena NagarajanWatson Health, IBM Almaden Research Center, San Jose, CA 95120.
Ilya B NovikovDepartment of Human and Molecular Genetics, Baylor College of Medicine, Houston, TX 77030.
Benjamin J BachmanDepartment of Human and Molecular Genetics, Baylor College of Medicine, Houston, TX 77030.
Sung Yun JungDepartment of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030.
Peter J HaasWatson Health, IBM Almaden Research Center, San Jose, CA 95120.
Jacques L LabrieWatson Health, IBM Almaden Research Center, San Jose, CA 95120.
Curtis R PickeringM.D. Anderson Cancer Center, The University of Texas, Houston, TX 77030.
Anbu K AdikesavanDepartment of Human and Molecular Genetics, Baylor College of Medicine, Houston, TX 77030.
Sam RegenbogenDepartment of Pharmacology, Baylor College of Medicine, Houston, TX 77030.
Linda KatoWatson Health, IBM Almaden Research Center, San Jose, CA 95120.
Ana LelescuWatson Health, IBM Almaden Research Center, San Jose, CA 95120.
Christie M BuchoveckyDepartment of Human and Molecular Genetics, Baylor College of Medicine, Houston, TX 77030.
Houyin ZhangDepartment of Human and Molecular Genetics, Baylor College of Medicine, Houston, TX 77030.
Sheng Hua BaoWatson Health, IBM Almaden Research Center, San Jose, CA 95120.
Stephen BoyerWatson Health, IBM Almaden Research Center, San Jose, CA 95120.
Griff WeberWatson Health, IBM Almaden Research Center, San Jose, CA 95120.
Kenneth L ScottDepartment of Human and Molecular Genetics, Baylor College of Medicine, Houston, TX 77030.
Ying ChenWatson Health, IBM Almaden Research Center, San Jose, CA 95120.
Scott SpanglerWatson Health, IBM Almaden Research Center, San Jose, CA 95120.
Lawrence A DonehowerDepartment of Molecular Virology and Microbiology, Baylor College of Medicine, Houston, TX 77030; larryd@bcm.tmc.edu lichtarge@bcm.edu.
Olivier LichtargeDepartment of Human and Molecular Genetics, Baylor College of Medicine, Houston, TX 77030; larryd@bcm.tmc.edu lichtarge@bcm.edu.
Baylor College of Medicine · USIBM Research - Almaden · USThe University of Texas MD Anderson Cancer Center · US

Funding

Tumor BiologyP30CA125123 · NCI · BAYLOR COLLEGE OF MEDICINE · PI DAVID R ROWLEY · 2007 to 2026
$73.9M
Tissue Analysis & Molecular Imaging CoreP30DK056338 · NIDDK · BAYLOR COLLEGE OF MEDICINE · PI Geoffrey A Preidis · 2001 to 2026
$28.3M
TRANSGENIC MOUSE MODELS TO STUDY OVARIAN FUNCTIONU54HD007495 · NICHD · BAYLOR COLLEGE OF MEDICINE · PI MANCINI, MICHAEL A. · 1998 to 2013
$19.9M
PROGRAM IN HUMAN MOLECULAR GENETICST32GM008307 · NIGMS · BAYLOR COLLEGE OF MEDICINE · PI SHAULSKY, GAD · 1990 to 2020
$6.6M
Comparative genomics of protein structure and functionR01GM079656 · NIGMS · BAYLOR COLLEGE OF MEDICINE · PI LICHTARGE, OLIVIER · 2007 to 2019
$4.3M
GROWTH AND DIFFERENTIATION IN EUKARYOTEST32HD007495 · NICHD · SALK INSTITUTE FOR BIOLOGICAL STUDIES · PI KINTNER, CHRISTOPHER ROBERT · 1995 to 2009
$1.6M
TRANSGENIC ANIMAL--COREP30HD007495 · NICHD · BAYLOR COLLEGE OF MEDICINE · PI GLASSER, STANLEY R · 1985 to 1997
–
NCI NIH HHS P30 CA125123NICHD NIH HHS P30 HD007495NICHD NIH HHS T32 HD007495NICHD NIH HHS U54 HD007495NIDDK NIH HHS P30 DK056338NIGMS NIH HHS R01 GM079656NIGMS NIH HHS T32 GM008307
6 · The paper itself

Abstract

Scientific progress depends on formulating testable hypotheses informed by the literature. In many domains, however, this model is strained because the number of research papers exceeds human readability. Here, we developed computational assistance to analyze the biomedical literature by reading PubMed abstracts to suggest new hypotheses. The approach was tested experimentally on the tumor suppressor p53 by ranking its most likely kinases, based on all available abstracts. Many of the best-ranked kinases were found to bind and phosphorylate p53 (

Indexed as

Abstracting and IndexingHCT116 CellsHEK293 CellsHumansNatural Language ProcessingNIMA-Related KinasesPhosphorylationPubMedTumor Suppressor Protein p53NEK2 protein, humanNIMA-Related KinasesTP53 protein, humanTumor Suppressor Protein p53automated hypothesis generationkinaseliterature text miningp53 inhibitionprotein–protein interaction

Identifiers

PMID30266789
PMCPMC6196525
OpenAlexW2892667257

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.