Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
National University Health System (NUHS), Singapore, Singapore.
Sci Rep. 2024 Sep 30;14(1):22618. doi: 10.1038/s41598-024-71422-2.
The detection of cancer-driving mutations is important for understanding cancer pathology and therapeutics development. Prediction tools have been created to streamline the computation process. However, most tools available have heterogeneous sensitivity or specificity. We built a machine learning-derived algorithm, DriverDetect that combines the outputs of seven pre-existing tools to improve the prediction of candidate driver cancer mutations. The algorithm was trained with cancer gene-specific mutation datasets of cancer patients to identify cancer drivers. DriverDetect performed better than the individual tools or their combinations in the validation test. It has the potential to incorporate future novel prediction algorithms and can be retrained with new datasets, offering an expanded application to pan-cancer analysis for cross-cancer study. (115 words).
检测致癌突变对于理解癌症发病机制和治疗药物开发非常重要。预测工具的出现简化了计算过程。然而，现有的大多数工具的敏感性或特异性存在差异。我们构建了一种基于机器学习的算法 DriverDetect，它结合了七种现有工具的输出结果，以提高候选驱动性癌症突变的预测能力。该算法使用癌症患者的特定癌症基因的突变数据集进行训练，以识别癌症驱动基因。在验证测试中，DriverDetect 的表现优于单个工具或它们的组合。它有可能整合未来新的预测算法，并可使用新数据集重新训练，从而为跨癌症研究的泛癌症分析提供更广泛的应用。（115 个单词）
