ArticleComputational and mathematical methods in medicine2021
Research on Cancer Molecular Typing Based on High-Throughput Sequencing Technology.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed.
- The B-cell-autoantibody axis in lung cancer immunity.Theranostics · 2026Review
- Retracted: Research on Cancer Molecular Typing Based on High-Throughput Sequencing Technology.Computational and mathematical methods in medicine · 2022Article
Corrections and comments
- Retraction · 2022-12-28Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Objections by Author(s) ·
- Retracted
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper studies the role of high-throughput measurement technology in cancer molecular typing. Based on the Dendrix algorithm, the model proposed in this paper selects the gene replication time as an inherent attribute that affects the frequency of gene mutations and adds it to the model. After setting the size of the gene set, compared with the Dendrix algorithm, the model does not need to delete the gene set that has been found in the process of searching the pathway, and it can find more driving pathway gene sets. Based on the high coverage and high exclusivity of the driving gene set in the pathway and the influence of gene covariates, this paper constructs an adaptive multiobjective optimization model. In order to overcome the problem of gene mutation heterogeneity, this model introduces gene covariates as the weight of gene mutation frequency so that the model is adaptive to each gene. The analysis of the research results shows the reliability of high-throughput sequencing technology.
Indexed as
Identifiers
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Registered trials
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.