Evidence map›Paper›PMID 37558767›Full record

ArticleJournal of cancer research and clinical oncology2023

Development and verification of a deep learning-based m

Siteng Chen, Encheng Zhang, Tuanjie Guo, Tao Wang, Jinyuan Chen, Ning Zhang, Xiang Wang, Junhua Zheng

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 41% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

8 authors at 4 institutions in 1 country.

Siteng Chen *Department of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Encheng Zhang *Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Tuanjie Guo *Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Tao WangDepartment of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jinyuan ChenDepartment of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ning ZhangDepartment of Urology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Xiang WangDepartment of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. seanw_hs@163.com.
Junhua ZhengDepartment of Urology, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shanghai First People's Hospital · CNShanghai Jiao Tong University · CNRenji Hospital · CNRuijin Hospital · CN

Funding

National Natural Science Foundation of China 81972393Promotion Project for National Foundation of Renji Hospital RJTJ23-PY-022
6 · The paper itself

Abstract

backgroundThe deep learning-based m

methodsA high-level neural network based on deep learning algorithm was applied to construct the m

resultsThe deep learning-based model performs well in predicting the survival status in 5-year follow-up, which also could significantly distinguish the patients with high overall survival risk in two independent patient cohort and a pan-cancer patient cohort. METTL14 deficiency could promote the migration and proliferation of renal cancer cells. In addition, our study also illustrated that METTL14 might participate in the regulation of circRNA in RCC.

conclusionsIn summary, we developed and verified a deep learning-based m

Indexed as

Deep learningm6AMETTL14PrognosisRenal cell carcinoma

Identifiers

PMID37558767
PMCPMC11797350
OpenAlexW4385685603

What OpenQuestion holds

Textmetadata
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.