Yu Na, Gao Ying-Lian, Liu Jin-Xing, Shang Junliang, Zhu Rong, Dai Ling-Yun
School of Information Science and Engineering, Qufu Normal University, Rizhao 276826, China.
Library of Qufu Normal University, Qufu Normal University, Rizhao 276826, China.
Genes (Basel). 2018 Nov 28;9(12):586. doi: 10.3390/genes9120586.
Cancer genomic data contain views from different sources that provide complementary information about genetic activity. This provides a new way for cancer research. Feature selection and multi-view clustering are hot topics in bioinformatics, and they can make full use of complementary information to improve the effect. In this paper, a novel integrated model called Multi-view Non-negative Matrix Factorization (MvNMF) is proposed for the selection of common differential genes (co-differential genes) and multi-view clustering. In order to encode the geometric information in the multi-view genomic data, graph regularized MvNMF (GMvNMF) is further proposed by applying the graph regularization constraint in the objective function. GMvNMF can not only obtain the potential shared feature structure and shared cluster group structure, but also capture the manifold structure of multi-view data. The validity of the proposed GMvNMF method was tested in four multi-view genomic data. Experimental results showed that the GMvNMF method has better performance than other representative methods.
癌症基因组数据包含来自不同来源的视图，这些视图提供了有关基因活性的补充信息。这为癌症研究提供了一种新方法。特征选择和多视图聚类是生物信息学中的热门话题，它们可以充分利用补充信息来提高效果。本文提出了一种名为多视图非负矩阵分解（MvNMF）的新型集成模型，用于选择共同差异基因（共差异基因）和多视图聚类。为了编码多视图基因组数据中的几何信息，通过在目标函数中应用图正则化约束，进一步提出了图正则化MvNMF（GMvNMF）。GMvNMF不仅可以获得潜在的共享特征结构和共享聚类组结构，还可以捕获多视图数据的流形结构。在所提出的GMvNMF方法的有效性在四个多视图基因组数据中进行了测试。实验结果表明，GMvNMF方法比其他代表性方法具有更好的性能。
