Evidence map›Paper›PMID 33402669›Full record

ReviewSignal transduction and targeted therapy2021

A new precision medicine initiative at the dawn of exascale computing.

Ruth Nussinov, Hyunbum Jang, Guy Nir, Chung-Jung Tsai, Feixiong Cheng

Open access · goldAbstract readReview
In one paragraph

Review in Signal transduction and targeted therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
3.8field-weighted citation impact, top 5% 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

26 citing papers in PubMed, 52 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Tumors and their microenvironments: Learning from pediatric brain pathologies.Biochimica et biophysica acta. Reviews on cancer · 2025
    Review
  6. Review
  7. Molecular principles underlying aggressive cancers.Signal transduction and targeted therapy · 2025
    Review
  8. Article
  9. A protracted war against cancer drug resistance.Cancer cell international · 2024
    Review
  10. Slower CDK4 and faster CDK2 activation in the cell cycle.Structure (London, England : 1993) · 2024
    Article
  11. Review
  12. Review
  13. Genomic Landscape and Potential Regulation of RNA Editing in Drug Resistance.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023
    Article
  14. Review
  15. Review
  16. Article
  17. AlphaFold, Artificial Intelligence (AI), and Allostery.The journal of physical chemistry. B · 2022
    Review
  18. Article
  19. Review
  20. Review
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 4 institutions in 2 countries.

Ruth NussinovComputational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA. NussinoR@mail.nih.gov.
Hyunbum JangComputational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA.
Guy NirDepartment of Genetics, Harvard Medical School, Boston, MA, 02115, USA.ORCID 0000-0001-9268-6596
Chung-Jung TsaiComputational Structural Biology Section, Frederick National Laboratory for Cancer Research in the Laboratory of Cancer Immunometabolism, National Cancer Institute, Frederick, MD, 21702, USA.
Feixiong ChengGenomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44106, USA.
Frederick National Laboratory for Cancer Research · USCleveland Clinic Lerner College of Medicine · USHarvard University · USTel Aviv University · IL

Funding

Protein Structure, Stability, and Amyloid FormationZIABC010440 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI NUSSINOV, RUTH · 2009 to 2025
$11.8M
Biomolecular Recognition and Binding MechanismsZIABC010441 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI NUSSINOV, RUTH · 2009 to 2025
$9.4M
CCR NIH HHS HHSN261200800001CNCI NIH HHS HHSN261200800001E
6 · The paper itself

Abstract

Which signaling pathway and protein to select to mitigate the patient's expected drug resistance? The number of possibilities facing the physician is massive, and the drug combination should fit the patient status. Here, we briefly review current approaches and data and map an innovative patient-specific strategy to forecast drug resistance targets that centers on parallel (or redundant) proliferation pathways in specialized cells. It considers the availability of each protein in each pathway in the specific cell, its activating mutations, and the chromatin accessibility of its encoding gene. The construction of the resulting Proliferation Pathway Network Atlas will harness the emerging exascale computing and advanced artificial intelligence (AI) methods for therapeutic development. Merging the resulting set of targets, pathways, and proteins, with current strategies will augment the choice for the attending physicians to thwart resistance.

Indexed as

Artificial IntelligenceComputer SystemsModels, BiologicalPrecision MedicineHumans

Identifiers

PMID33402669
PMCPMC7785737
OpenAlexW3120989527

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.