Evidence map›Paper›PMID 38136405›Full record

ReviewCancers2023

Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends.

Grigoriy Gogoshin, Andrei S Rodin

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
  5. Review
  6. Review
  7. Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Review
  15. Review
  16. Review
  17. Article
  18. Review
  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

2 authors.

Grigoriy GogoshinDepartment of Computational and Quantitative Medicine, Beckman Research Institute, and Diabetes and Metabolism Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA.ORCID 0000-0003-0675-1799
Andrei S RodinDepartment of Computational and Quantitative Medicine, Beckman Research Institute, and Diabetes and Metabolism Research Institute, City of Hope National Medical Center, 1500 East Duarte Road, Duarte, CA 91010, USA.ORCID 0000-0002-2570-0332

Funding

An integrated toolkit combining computational systems biology techniques with molecular dynamics simulations to delineate functionality of GPCRsR01LM013876 · NLM · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · PI RODIN, ANDREI, VAIDEHI, NAGARAJAN · 2022 to 2025
$1.5M
Scalable Bayesian Network analysis of multimodal FACS and SUMOylation data, with generalization to other big mixed biological datasetsR01LM013138 · NLM · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · PI RODIN, ANDREI · 2020 to 2022
$776k
NIH HHS R01LM013138NLM NIH HHS R01 LM013138NLM NIH HHS R01 LM013876
6 · The paper itself

Abstract

Next-generation cancer and oncology research needs to take full advantage of the multimodal structured, or graph, information, with the graph data types ranging from molecular structures to spatially resolved imaging and digital pathology, biological networks, and knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on large multimodal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. We then identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise guidelines for cancer and oncology researchers or physician-scientists, asking the question of whether they should adopt the GNN methodology in their research pipelines.

Indexed as

Bayesian networkcancerdeep learningGNNgraphical modelgraph neural networkoncology

Identifiers

PMID38136405
PMCPMC10742144

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.