论文标题
RandomScm：针对OMICS量身定制的稀疏分类器的可解释合奏
RandomSCM: interpretable ensembles of sparse classifiers tailored for omics data
论文作者
论文摘要
背景：理解OMICS与表型之间的关系是精确医学中的核心问题。代谢组学数据的高维度挑战学习算法，从可扩展性和概括方面进行学习。大多数学习算法不会产生可解释的模型 - 方法：我们根据决策规则的结合或析取建议了一种集合学习算法。 - 结果：代谢组学数据的应用显示，它会产生可实现高预测性能的模型。这些模型的解释性使其可用于高维数据中的生物标志物发现和模式发现。
Background: Understanding the relationship between the Omics and the phenotype is a central problem in precision medicine. The high dimensionality of metabolomics data challenges learning algorithms in terms of scalability and generalization. Most learning algorithms do not produce interpretable models -- Method: We propose an ensemble learning algorithm based on conjunctions or disjunctions of decision rules. -- Results : Applications on metabolomics data shows that it produces models that achieves high predictive performances. The interpretability of the models makes them useful for biomarker discovery and patterns discovery in high dimensional data.
