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Application of Machine Learning to Development of Copy Number Variation-based Prediction of Cancer Risk:

机译：机器学习在基于拷贝数变异的癌症风险预测中的应用：

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In the present study, recurrent copy number variations (CNVs) from non-tumor blood cell DNAs of Caucasian non-cancer subjects and glioma, myeloma, and colorectal cancer-patients, and Korean non-cancer subjects and hepatocellular carcinoma, gastric cancer, and colorectal cancer patients, were found to reveal for each of the two ethnic cohorts highly significant differences between cancer patients and controls with respect to the number of CN-losses and size-distribution of CN-gains, suggesting the existence of recurrent constitutional CNV-features useful for prediction of predisposition to cancer. Upon identification by machine learning, such CNV-features could extensively discriminate between cancer-patient and control DNAs. When the CNV-features selected from a learning-group of Caucasian or Korean mixed DNAs consisting of both cancer-patient and control DNAs were employed to make predictions on the cancer predisposition of an unseen test group of mixed DNAs, the average prediction accuracy was 93.6% for the Caucasian cohort and 86.5% for the Korean cohort.
机译：在本研究中，来自白种人非癌症对象和神经胶质瘤，骨髓瘤和结直肠癌患者以及韩国非癌症对象和肝细胞癌，胃癌和非癌症患者的非肿瘤血细胞DNA的重复拷贝数变异（CNV）。结直肠癌患者被发现揭示了两个种族队列中每一个在癌症患者和对照组之间在CN丢失数量和CN增益大小分布方面的高度显着差异，这表明存在复发性CNV构成特征可用于预测癌症的易感性。通过机器学习进行识别后，此类CNV功能可广泛区分癌症患者和对照DNA。当使用从由癌症患者和对照DNA组成的高加索人或韩国人混合DNA的学习组中选择的CNV特征来预测一个看不见的混合DNA测试组的癌症易感性时，平均预测准确性为93.6高加索人占30％，韩国人占86.5％。

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