论文标题
parea：癌症亚型发现的多视图合奏聚类
Parea: multi-view ensemble clustering for cancer subtype discovery
论文作者
论文摘要
多视图聚类方法对于将患者分层分为具有相似分子特征的亚组至关重要。近年来，为此目的开发了广泛的方法。但是，由于癌症相关数据的多样性多样性，在所有情况下，单一方法的表现都不足够好。我们提出了Parea，这是一种用于疾病亚型发现的多视图分层集合聚类方法。我们在几个机器学习基准数据集上演示了其性能。我们将我们的方法应用于现实世界多视频癌患者数据。 Parea的表现优于七种分析的癌症类型中的六种。我们已经将Parea方法集成到了开发的Python软件包Pyrea（https://github.com/mdbloice/pyrea）中，该方法可以轻松而灵活地设计集成工作流程，同时结合了广泛的融合和聚类算法。
Multi-view clustering methods are essential for the stratification of patients into sub-groups of similar molecular characteristics. In recent years, a wide range of methods has been developed for this purpose. However, due to the high diversity of cancer-related data, a single method may not perform sufficiently well in all cases. We present Parea, a multi-view hierarchical ensemble clustering approach for disease subtype discovery. We demonstrate its performance on several machine learning benchmark datasets. We apply and validate our methodology on real-world multi-view cancer patient data. Parea outperforms the current state-of-the-art on six out of seven analysed cancer types. We have integrated the Parea method into our developed Python package Pyrea (https://github.com/mdbloice/Pyrea), which enables the effortless and flexible design of ensemble workflows while incorporating a wide range of fusion and clustering algorithms.
