2026-10-14T22:53:45Z https://www.scidb.cn/oai

10.57760/sciencedb.05737 2022-10-31T09:08:11Z

2022-10-31 Prediction model for malignant pulmonary nodules based on cfMeDIP-seq and machine learning doi:10.57760/sciencedb.05737 en Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) is a new bisulfite-free technique, which can detect the whole-genome methylation of blood cell-free DNA (cfDNA). Using this technique, we identified differentially methylated regions (DMR) of cfDNA between lung tumors and normal controls. Based on the top 300 DMR, we built a random forest prediction model, which was able to distinguish malignant lung tumors from normal controls with high sensitivity and specificity of 91.0% and 93.3% (AUROC curve of 0.963). In summary, we reported a non–invasive prediction model that had good ability to distinguish malignant pulmonary nodules. malignant pulmonary nodules; cfMeDIP-seq; machine learning JianQi bo hong Jinfu Nie PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/10.1111/cas.15052 Science Data Bank