Tri-regularized nonnegative matrix tri-factorization for co-clustering

Hypergraph regularized nonnegative triple decomposition for multiway data analysis

Tucker decomposition is widely used for image representation, data reconstruction, and machine learning tasks, but the calculation cost for updating the Tucker core is high. Bilevel form of triple decomposition (TriD) overcomes this issue by decomposing the Tucker core into three low-dimensional third-order factor tensors and plays an important role in the dimension reduction of data representation. TriD, on the other hand, is incapable of precisely encoding similarity relationships for tensor data with a complex manifold structure. To address this shortcoming, we take advantage of hypergraph learning and propose a novel hypergraph regularized nonnegative triple decomposition for multiway data analysis that employs the hypergraph to model the complex relationships among the raw data. Furthermore, we develop a multiplicative update algorithm to solve our optimization problem and theoretically prove its convergence. Finally, we perform extensive numerical tests on six real-world datasets, and the results show that our proposed algorithm outperforms some state-of-the-art methods.

The Rare and Atypical Diabetes Network (RADIANT) Study: Design and Early Results

OBJECTIVE

The Rare and Atypical Diabetes Network (RADIANT) will perform a study of individuals and, if deemed informative, a study of their family members with uncharacterized forms of diabetes.

RESEARCH DESIGN AND METHODS

The protocol includes genomic (whole-genome [WGS], RNA, and mitochondrial sequencing), phenotypic (vital signs, biometric measurements, questionnaires, and photography), metabolomics, and metabolic assessments.

RESULTS

Among 122 with WGS results of 878 enrolled individuals, a likely pathogenic variant in a known diabetes monogenic gene was found in 3 (2.5%), and six new monogenic variants have been identified in the SMAD5, PTPMT1, INS, NFKB1, IGF1R, and PAX6 genes. Frequent phenotypic clusters are lean type 2 diabetes, autoantibody-negative and insulin-deficient diabetes, lipodystrophic diabetes, and new forms of possible monogenic or oligogenic diabetes.

CONCLUSIONS

The analyses will lead to improved means of atypical diabetes identification. Genetic sequencing can identify new variants, and metabolomics and transcriptomics analysis can identify novel mechanisms and biomarkers for atypical disease.

NMTF-DTI: A Nonnegative Matrix Tri-factorization Approach With Multiple Kernel Fusion for Drug-Target Interaction Prediction

Prediction of drug-target interactions (DTIs) plays a significant role in drug development and drug discovery. Although this task requires a large investment in terms of time and cost, especially when it is performed experimentally, the results are not necessarily significant. Computational DTI prediction is a shortcut to reduce the risks of experimental methods. In this study, we propose an effective approach of nonnegative matrix tri-factorization, referred to as NMTF-DTI, to predict the interaction scores between drugs and targets. NMTF-DTI utilizes multiple kernels (similarity measures) for drugs and targets and Laplacian regularization to boost the prediction performance. The performance of NMTF-DTI is evaluated via cross-validation and is compared with existing DTI prediction methods in terms of the area under the receiver operating characteristic (ROC) curve (AUC) and the area under the precision and recall curve (AUPR). We evaluate our method on four gold standard datasets, comparing to other state-of-the-art methods. Cross-validation and a separate, manually created dataset are used to set parameters. The results show that NMTF-DTI outperforms other competing methods. Moreover, the results of a case study also confirm the superiority of NMTF-DTI.