A Fast Quantum Clustering Approach for Cancer Gene Clustering

At present, the main problem of gene expression data processing is how to use effective analytical methods to analyze gene expression data to obtain useful information. A large amount of useful information can be obtained by cluster analysis of gene expression data, which provides a basis for biology to predict cell cycle, predict the gene function, discover disease-causing genes and explain some new disease types. In this paper, a fast clustering method is proposed, which effectively combines the advantages of the graph regularized non-negative matrices factorization (GNMF) method with Quantum Clustering (QC) method, effectively improving the running speed of existing clustering methods and improving the clustering accuracy to some extent.