Integrating multigenomic data to recognize cancer subtype is an important task in bioinformatics. In recent years, some multiview clustering algorithms have been proposed and applied to identify cancer subtype. However, these clustering algorithms ignore that each data contributes differently to the clustering results during the fusion process, and they require additional clustering steps to generate the final labels. In this paper, a new one-step method for cancer subtype recognition based on graph learning framework is designed, called Laplacian Rank Constrained Multiview Clustering (LRCMC). LRCMC first forms a graph for a single biological data to reveal the relationship between data points and uses affinity matrix to encode the graph structure. Then, it adds weights to measure the contribution of each graph and finally merges these individual graphs into a consensus graph. In addition, LRCMC constructs the adaptive neighbors to adjust the similarity of sample points, and it uses the rank constraint on the Laplacian matrix to ensure that each graph structure has the same connected components. Experiments on several benchmark datasets and The Cancer Genome Atlas (TCGA) datasets have demonstrated the effectiveness of the proposed algorithm comparing to the state-of-the-art methods.
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