Evidence map›Paper›PMID 36081436›Full record

ArticleComputational and mathematical methods in medicine2022

Optimal Modeling of Anti-Breast Cancer Candidate Drugs Based on Graph Model Feature Selection.

Rongyuan Chen, Zhixiong He, Shaonian Huang, Lizhi Shen, Xiancheng Zhou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Rongyuan ChenKey Laboratory of Hunan Province for Statistical Learning and Intelligent Computation, Hunan University of Technology and Business, Hunan Changsha 410205, China.ORCID https://orcid.org/0000-0001-5048-6174
Zhixiong HeHunan University of Technology and Business, Hunan Changsha 410205, China.ORCID https://orcid.org/0000-0001-5243-7907
Shaonian HuangKey Laboratory of Hunan Province for Statistical Learning and Intelligent Computation, Hunan University of Technology and Business, Hunan Changsha 410205, China.ORCID https://orcid.org/0000-0003-1453-2297
Lizhi ShenKey Laboratory of Hunan Province for Statistical Learning and Intelligent Computation, Hunan University of Technology and Business, Hunan Changsha 410205, China.ORCID https://orcid.org/0000-0003-2872-787X
Xiancheng ZhouKey Laboratory of Hunan Province for Statistical Learning and Intelligent Computation, Hunan University of Technology and Business, Hunan Changsha 410205, China.ORCID https://orcid.org/0000-0003-0134-2179

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Breast cancer is one of the most widespread and fatal cancers in women. At present, anticancer drug-inhibiting estrogen receptor

Indexed as

Estrogen Receptor alphaNeoplasmsAlgorithmsFemaleHumansPrincipal Component AnalysisSupport Vector MachineEstrogen Receptor alpha

Identifiers

PMID36081436
PMCPMC9448531

What OpenQuestion holds

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LicenceCC BY
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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.