Shuli Kang, Qingjiao Li, Quan Chen, Yonggang Zhou, Stacy Park, Gina Lee, Brandon Grimes, Kostyantyn Krysan, Min Yu, Wei Wang, Frank Alber, Fengzhu Sun, Steven M. Dubinett, Wenyuan Li, Xianghong Jasmine Zhou
Abstract
We propose a probabilistic method, CancerLocator, which exploits the diagnostic potential of cell-free DNA by determining not only the presence but also the location of tumors. CancerLocator simultaneously infers the proportions and the tissue-of-origin of tumor-derived cell-free DNA in a blood sample using genome-wide DNA methylation data. CancerLocator outperforms two established multi-class classification methods on simulations and real data, even with the low proportion of tumor-derived DNA in the cell-free DNA scenarios. CancerLocator also achieves promising results on patient plasma samples with low DNA methylation sequencing coverage.
This research output has an Altmetric Attention Score of 318. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 June 2024.
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#114,391
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#1
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Altmetric has tracked 26,245,199 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,642 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.4. This one has done particularly well, scoring higher than 99% of its peers.
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We're also able to compare this research output to 59 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.
