Multi-View Spectral Clustering Based on Multi-Smooth Representation Fusion for Cancer Subtype Prediction.

ABSTRACT: It is a vital task to design an integrated machine learning model to discover cancer subtypes and understand the heterogeneity of cancer based on multiple omics data. In recent years, some multi-view clustering algorithms have been proposed and applied to the prediction of cancer subtypes. Among them, the multi-view clustering methods based on graph learning are widely concerned. These multi-view approaches usually have one or more of the following problems. Many multi-view algorithms use the original omics data matrix to construct the similarity matrix and ignore the learning of the similarity matrix. They separate the data clustering process from the graph learning process, resulting in a highly dependent clustering performance on the predefined graph. In the process of graph fusion, the

SUBMITTER: Liu J 

PROVIDER: S-EPMC8450448 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature