Evidence map›Paper›PMID 33986671›Full record

ArticleFrontiers in pharmacology2021

Prediction of Synergistic Drug Combinations for Prostate Cancer by Transcriptomic and Network Characteristics.

Shiqi Li, Fuhui Zhang, Xiuchan Xiao, Yanzhi Guo, Zhining Wen, Menglong Li, Xuemei Pu

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

Article in Frontiers in pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.4field-weighted citation impact, top 10% 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

13 citing papers in PubMed, 19 citations in OpenAlex.

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  12. Systematic review of computational methods for drug combination prediction.Computational and structural biotechnology journal · 2022
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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

7 authors at 2 institutions in 1 country.

Shiqi LiCollege of Chemistry, Sichuan University, Chengdu, China.
Fuhui ZhangCollege of Chemistry, Sichuan University, Chengdu, China.
Xiuchan XiaoSchool of Material Science and Environmental Engineering, Chengdu Technological University, Chengdu, China.
Yanzhi GuoCollege of Chemistry, Sichuan University, Chengdu, China.
Zhining WenCollege of Chemistry, Sichuan University, Chengdu, China.
Menglong LiCollege of Chemistry, Sichuan University, Chengdu, China.
Xuemei PuCollege of Chemistry, Sichuan University, Chengdu, China.
Sichuan University · CNChengdu Technological University

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prostate cancer (PRAD) is a major cause of cancer-related deaths. Current monotherapies show limited efficacy due to often rapidly emerging resistance. Combination therapies could provide an alternative solution to address this problem with enhanced therapeutic effect, reduced cytotoxicity, and delayed the appearance of drug resistance. However, it is prohibitively cost and labor-intensive for the experimental approaches to pick out synergistic combinations from the millions of possibilities. Thus, it is highly desired to explore other efficient strategies to assist experimental researches. Inspired by the challenge, we construct the transcriptomics-based and network-based prediction models to quickly screen the potential drug combination for Prostate cancer, and further assess their performance by

Indexed as

computationdrug combinationsprostate cancerthe network-based predictionthe transcriptomics-based prediction

Identifiers

PMID33986671
PMCPMC8112211
OpenAlexW3154181963

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.