Patient stratification has been studied widely to tackle subtype diagnosis problems for effective treatment. Due to the dimensionality curse and poor interpretability of data, there is always a long-lasting challenge in constructing a stratification model with high diagnostic ability and good generalization. To address these problems, this article proposes two novel evolutionary multiobjective clustering algorithms with ensemble (NSGA-II-ECFE and MOEA/D-ECFE) with four cluster validity indices used as the objective functions. First, an effective ensemble construction method is developed to enrich the ensemble diversity. After that, an ensemble clustering fitness evaluation (ECFE) method is proposed to evaluate the ensembles by measuring the consensus clustering under those four objective functions. To generate the consensus clustering, ECFE exploits the hybrid co-association matrix from the ensembles and then dynamically selects the suitable clustering algorithm on that matrix. Multiple experiments have been conducted to demonstrate the effectiveness of the proposed algorithm in comparison with seven clustering algorithms, twelve ensemble clustering approaches, and two multiobjective clustering algorithms on 55 synthetic datasets and 35 real patient stratification datasets. The experimental results demonstrate the competitive edges of the proposed algorithms over those compared methods. Furthermore, the proposed algorithm is applied to extend its advantages by identifying cancer subtypes from five cancer-related single-cell RNA-seq datasets.

中文翻译：

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用于患者分层的集成进化多目标聚类算法


患者分层已被广泛研究，以解决亚型诊断问题，从而实现有效治疗。由于维数诅咒和数据的可解释性差，构建具有高诊断能力和良好泛化能力的分层模型始终面临着长期的挑战。为了解决这些问题，本文提出了两种新颖的集成进化多目标聚类算法（NSGA-II-ECFE 和 MOEA/D-ECFE），以四个聚类有效性指标作为目标函数。首先，开发了一种有效的集成构建方法来丰富集成多样性。之后，提出了一种集成聚类适应度评估（ECFE）方法，通过测量这四个目标函数下的一致性聚类来评估集成。为了生成共识聚类，ECFE 利用集成中的混合共关联矩阵，然后在该矩阵上动态选择合适的聚类算法。我们在 55 个合成数据集和 35 个真实患者分层数据集上进行了多次实验，以证明该算法与 7 种聚类算法、12 种集成聚类方法和两种多目标聚类算法相比的有效性。实验结果证明了所提出的算法相对于那些比较方法的竞争优势。此外，所提出的算法通过从五个癌症相关的单细胞 RNA-seq 数据集中识别癌症亚型来扩展其优势。