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Multiple kernel learning for integrative consensus clustering of 'omic datasets.
Bioinformatics ( IF 4.4 ) Pub Date : 2020-06-27 , DOI: 10.1093/bioinformatics/btaa593
Alessandra Cabassi 1 , Paul D W Kirk 1, 2
Affiliation  

Diverse applications – particularly in tumour subtyping – have demonstrated the importance of integrative clustering techniques for combining information from multiple data sources. Cluster Of Clusters Analysis (COCA) is one such approach that has been widely applied in the context of tumour subtyping. However, the properties of COCA have never been systematically explored, and its robustness to the inclusion of noisy datasets is unclear.

中文翻译:

用于“组学数据集”综合共识聚类的多核学习。

不同的应用——特别是在肿瘤亚型分析中——已经证明了整合聚类技术对于组合来自多个数据源的信息的重要性。聚类分析(COCA)就是这样一种方法,已广泛应用于肿瘤亚型分析。然而,COCA 的特性从未被系统地探索过,并且其对包含噪声数据集的鲁棒性尚不清楚。
更新日期:2020-06-27
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