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Background: There are correlations between the multiple types of data stored in the tensor space. The matrix formed by the data in the high-dimensional space is of low rank. Therefore, the potential association between genes and cancers can be explored in low-rank space. Tensor robust principal component analysis (TRPCA) is used to extract information by obtaining coefficient tensors with low-rank representation. In practical applications, global features and sparse structure are ignored, which leads to incomplete analysis. Objective: This paper proposes an adaptive reweighted TRPCA method (ARTRPCA) to explore cancer subtypes and identify conjoint abnormally expressed genes (CAEGs). Methods: ARTRCA analyzes data based on adaptive learning of primary information. Meanwhile, the weighting scheme based on singular value updates is used to learn global features in low-rank space. The reweighted I1 algorithm is based on prior knowledge, which is used to learn about sparse structures. Moreover, the sparsity threshold of Gaussian entries has been increased to reduce the influence of outliers. Results: In the experiment of sample clustering, ARTRPCA has obtained promising experimental results. The identified CAEGs are pathogenic genes of various cancers or are highly expressed in specific cancers. Conclusion: The ATRPCA method has shown excellent application prospects in cancer multiomics data.

With the development of bioinformatics, the important role played by lncRNAs in various intractable diseases has aroused the interest of many experts. In recent studies, researchers have found that several human diseases are related to lncRANs. Moreover, it is very difficult and expensive to explore the unknown lncRNA-disease associations (LDAs), so only a few associations have been confirmed. It is vital to find a more accurate and effective method to identify potential LDAs. In this study, a method of collaborative matrix factorization based on correntropy (LDCMFC) is proposed for the identification of potential LDAs. To improve the robustness of the algorithm, the traditional minimization of the Euclidean distance is replaced with the maximized correntropy. In addition, the weighted K nearest known neighbor (WKNKN) method is used to rebuild the adjacency matrix. Finally, the performance of LDCMFC is tested by 5-fold cross-validation. Compared with other traditional methods, LDACMFC obtains a higher AUC of 0.8628. In different types of studies of three important cancer cases, most of the potentially relevant lncRNAs derived from the experiments have been validated in the databases. The final result shows that LDCMFC is a feasible method to predict LDAs.