ArticleAnalytical science advances2025
Computational Framework for High Copy-Number Probe Selection and Cross-Binding Reduction.
Article in Analytical science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
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Who cites it
1 citing paper in PubMed.
- Computational Framework for High Copy-Number Probe Selection and Cross-Binding Reduction.Analytical science advances · 2025Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA probe design plays a critical role in biosensor-based disease diagnostics, gene expression analysis and environmental monitoring. Traditional probe designs primarily target lower-copy genetic sequences, often leading to low detection sensitivity due to limited hybridization events. This study introduces a novel probe design strategy that leverages highly repetitive DNA sequences as target sites to amplify biosensor signals without requiring PCR-based amplification. The computational selection process is conducted using a custom-developed bioinformatics tool to identify repetitive sequences across the entire
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Registered trials
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