BACKGROUND A variant of unknown significance (VUS) is a variant form of a gene that has been identified through genetic testing, but whose significance to the organism function is not known. An actual challenge in precision medicine is to precisely identify which detected mutations from a sequencing process have a suitable role in the treatment or diagnosis of a disease. The average accuracy of pathogenicity predictors is 85%. However, there is a significant discordance about the identification of mutational impact and pathogenicity among them. Therefore, manual verification is necessary for confirming the real effect of a mutation in its casuistic. METHODS In this work, we use variables categorization and selection for building a decision tree model, and later we measure and compare its accuracy with four known mutation predictors and seventeen supervised machine-learning (ML) algorithms. RESULTS The results showed that the proposed tree reached the highest precision among all tested variables: 91% for True Neutrals, 8% for False Neutrals, 9% for False Pathogenic, and 92% for True Pathogenic. CONCLUSIONS The decision tree exceptionally demonstrated high classification precision with cancer data, producing consistently relevant forecasts for the sample tests with an accuracy close to the best ones achieved from supervised ML algorithms. Besides, the decision tree algorithm is easier to apply in clinical practice by non-IT experts. From the cancer research community perspective, this approach can be successfully applied as an alternative for the determination of potential pathogenicity of VOUS.

中文翻译：

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提高临床实践中致病突变识别的决策树。


背景技术未知意义的变体(VUS)是通过基因测试鉴定出的基因的变体形式，但其对生物体功能的意义尚不清楚。精准医学的一个实际挑战是精确识别测序过程中检测到的哪些突变在疾病的治疗或诊断中具有适当的作用。致病性预测因子的平均准确度为 85%。然而，它们之间的突变影响和致病性的鉴定存在显着的不一致。因此，需要手动验证来确认突变的真实效果。方法在这项工作中，我们使用变量分类和选择来构建决策树模型，然后我们使用四个已知的突变预测器和十七个监督机器学习（ML）算法来测量和比较其准确性。结果 结果表明，所提出的树在所有测试变量中达到了最高的精度：真中性为 91%，假中性为 8%，假致病为 9%，真致病为 92%。结论 决策树在癌症数据方面表现出极高的分类精度，为样本测试提供一致的相关预测，其准确度接近监督 ML 算法实现的最佳预测。此外，决策树算法更容易被非IT专家应用于临床实践。从癌症研究界的角度来看，这种方法可以成功地应用于确定 VOUS 潜在致病性的替代方法。