Evidence map›Paper›PMID 36816042›Full record

ArticleFrontiers in genetics2023

Gamma distribution based predicting model for breast cancer drug response based on multi-layer feature selection.

Tongtong Cui, Zeyuan Wang, Hong Gu, Pan Qin, Jia Wang

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

Article in Frontiers in genetics, 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
1.0field-weighted citation impact, top 22% 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, 5 citations in OpenAlex.

  1. 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

5 authors at 3 institutions in 1 country.

Tongtong CuiFaculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China.
Zeyuan WangFaculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China.
Hong GuFaculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China.
Pan QinFaculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning, China.
Jia WangDepartment of Breast Surgery, Second Hospital of Dalian Medical University, Dalian, Liaoning, China.
Dalian University · CNDalian University of Technology · CNSecond Affiliated Hospital of Dalian Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the pursuit of precision medicine for cancer, a promising step is to predict drug response based on data mining, which can provide clinical decision support for cancer patients. Although some machine learning methods for predicting drug response from genomic data already exist, most of them focus on point prediction, which cannot reveal the distribution of predicted results. In this paper, we propose a three-layer feature selection combined with a gamma distribution based GLM and a two-layer feature selection combined with an ANN. The two regression methods are applied to the Encyclopedia of Cancer Cell Lines (CCLE) and the Cancer Drug Sensitivity Genomics (GDSC) datasets. Using ten-fold cross-validation, our methods achieve higher accuracy on anticancer drug response prediction compared to existing methods, with an

Indexed as

artificial neural networkbreast cancerdrug responsefeature selectiongeneralized linear modelmachine learning

Identifiers

PMID36816042
PMCPMC9932661
OpenAlexW4319000661

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.