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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Adaptive Fuzzy Consensus Clustering Framework for Clustering Analysis of Cancer Data
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Adaptive Fuzzy Consensus Clustering Framework for Clustering Analysis of Cancer Data

机译：癌症数据聚类分析的自适应模糊共识聚类框架

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摘要

Performing clustering analysis is one of the important research topics in cancer discovery using gene expression profiles, which is crucial in facilitating the successful diagnosis and treatment of cancer. While there are quite a number of research works which perform tumor clustering, few of them considers how to incorporate fuzzy theory together with an optimization process into a consensus clustering framework to improve the performance of clustering analysis. In this paper, we first propose a random double clustering based cluster ensemble framework (RDCCE) to perform tumor clustering based on gene expression data. Specifically, RDCCE generates a set of representative features using a randomly selected clustering algorithm in the ensemble, and then assigns samples to their corresponding clusters based on the grouping results. In addition, we also introduce the random double clustering based fuzzy cluster ensemble framework (RDCFCE), which is designed to improve the performance of RDCCE by integrating the newly proposed fuzzy extension model into the ensemble framework. RDCFCE adopts the normalized cut algorithm as the consensus function to summarize the fuzzy matrices generated by the fuzzy extension models, partition the consensus matrix, and obtain the final result. Finally, adaptive RDCFCE (A-RDCFCE) is proposed to optimize RDCFCE and improve the performance of RDCFCE further by adopting a self-evolutionary process (SEPP) for the parameter set. Experiments on real cancer gene expression profiles indicate that RDCFCE and A-RDCFCE works well on these data sets, and outperform most of the state-of-the-art tumor clustering algorithms.
机译：进行聚类分析是使用基因表达谱在癌症发现中的重要研究主题之一，这对促进癌症的成功诊断和治疗至关重要。尽管进行肿瘤聚类的研究工作很多，但很少有人考虑如何将模糊理论和优化过程结合到共识聚类框架中以提高聚类分析的性能。在本文中，我们首先提出一个基于随机双聚类的聚类集成框架（RDCCE），以基于基因表达数据进行肿瘤聚类。具体来说，RDCCE使用集合中的随机选择的聚类算法生成一组代表性特征，然后根据分组结果将样本分配给它们的相应聚类。此外，我们还介绍了基于随机双聚类的模糊聚类集成框架（RDCFCE），该框架旨在通过将新提出的模糊扩展模型集成到集成框架中来提高RDCCE的性能。 RDCFCE采用归一化割算法作为共识函数，对模糊扩展模型生成的模糊矩阵进行汇总，划分共识矩阵，得到最终结果。最后，提出了自适应RDCFCE（A-RDCFCE），通过对参数集采用自演化过程（SEPP）来优化RDCFCE并进一步提高RDCFCE的性能。对真实癌症基因表达谱的实验表明，RDCFCE和A-RDCFCE在这些数据集上效果很好，并且优于大多数最新的肿瘤聚类算法。

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