ArticleSheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi2023
[MinerVa: A high performance bioinformatic algorithm for the detection of minimal residual disease in solid tumors].
Article in Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors at 2 institutions in 1 country.
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Abstract
How to improve the performance of circulating tumor DNA (ctDNA) signal acquisition and the accuracy to authenticate ultra low-frequency mutation are major challenges of minimal residual disease (MRD) detection in solid tumors. In this study, we developed a new MRD bioinformatics algorithm, namely multi-variant joint confidence analysis (MinerVa), and tested this algorithm both in contrived ctDNA standards and plasma DNA samples of patients with early non-small cell lung cancer (NSCLC). Our results showed that the specificity of multi-variant tracking of MinerVa algorithm ranged from 99.62% to 99.70%, and when tracking 30 variants, variant signals could be detected as low as 6.3 × 10
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