Integrating multiple genomic imaging data for the study of lung metastasis in sarcomas using multi-dimensional constrained joint non-negative matrix factorization

Deng, Jin; Zeng, Weiming; Luo, Sizhe; Kong, Wei; Shi, Yuhu; Liu, Ying; Zhang, Hua

doi:10.1016/j.ins.2021.06.058

Cited by 22 publications

(11 citation statements)

References 38 publications

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“…Recently, to integrate pathological images of soft tissue sarcomas with two genetic data (DNA methylation and copy number variation), they proposed a Multi-Dimensional Joint Non-negative Matrix Factorization (MDJNMF) algorithm that integrates multiple biological empirical knowledge, the potential association pattern with the three kinds of data was found through multi-level analysis. The comprehensive prediction index AUC of the identified relevant biomarkers reached 0.8 ( Deng et al, 2021 ). The above matrix factorization correlation algorithms are based on linear assumptions and cannot consider the complex relationship between multi-omics data from a nonlinear perspective.…”

Section: Introductionmentioning

confidence: 99%

Integration of RNA molecules data with prior-knowledge driven Joint Deep Semi-Negative Matrix Factorization for heart failure study

Chen

Zhang

et al. 2022

Front. Genet.

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Heart failure (HF) is the main manifestation of cardiovascular disease. Recent studies have shown that various RNA molecules and their complex connections play an essential role in HF’s pathogenesis and pathological progression. This paper aims to mine key RNA molecules associated with HF. We proposed a Prior-knowledge Driven Joint Deep Semi-Negative Matrix Factorization (PD-JDSNMF) model that uses a hierarchical nonlinear feature extraction method that integrates three types of data: mRNA, lncRNA, and miRNA. The PPI information is added to the model as prior knowledge, and the Laplacian constraint is used to help the model resist the noise in the genetic data. We used the PD-JDSNMF algorithm to identify significant co-expression modules. The elements in the module are then subjected to bioinformatics analysis and algorithm performance analysis. The results show that the PD-JDSNMF algorithm can robustly select biomarkers associated with HF. Finally, we built a heart failure diagnostic model based on multiple classifiers and using the Top 13 genes in the significant module, the AUC of the internal test set was up to 0.8714, and the AUC of the external validation set was up to 0.8329, which further confirmed the effectiveness of the PD-JDSNMF algorithm.

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Section: Introductionmentioning

confidence: 99%

Integration of RNA molecules data with prior-knowledge driven Joint Deep Semi-Negative Matrix Factorization for heart failure study

Chen

Zhang

et al. 2022

Front. Genet.

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“…Finally, to dig deeper into the diagnostically to adapt to data types of different backgrounds and modalities. Deng et al [10] applied the algorithm to the imaging genetics of lung adenocarcinoma by fusing the characteristics of case images, copy number variation, and DNA methylation data and adding network regularization constraints based on the JNMF algorithm. To improve the degree of association between different data, orthogonal constraints are added to remove redundant features and reduce the algorithm' s time complexity.…”

Section: Methodsmentioning

confidence: 99%

An improved joint non-negative matrix factorization for identifying surgical treatment timing of neonatal necrotizing enterocolitis

Huang

Lai

et al. 2022

Bosn J of Basic Med Sci

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Neonatal necrotizing enterocolitis is a severe neonatal intestinal disease. Timely identification of surgical indications is essential for newborns in order to seek the best time for treatment and improve prognosis. This paper attempts to establish an algorithm model based on multimodal clinical data to determine the features of surgical indications and construct an auxiliary diagnosis model. The proposed algorithm adds hypergraph constraints on the two modal data based on Joint Nonnegative Matrix Factorization (JNMF), aiming to mine the higher-order correlations of the two data features. In addition, the adjacency matrix of the two kinds of data is used as a network regularization constraint to prevent overfitting. Orthogonal and L1-norm regulations were introduced to avoid feature redundancy and perform feature selection, respectively, and confirmed 14 clinical features. Finally, we used three classifiers, random forest, support vector machine, and logistic regression, to perform binary classification of patients requiring surgery. The results show that when the features selected by the proposed algorithm model are classified by random forest, the area under the ROC curve is 0.8, which has high prediction accuracy.

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“…It successfully verified the recognition of soft tissue sarcoma (STS) lung metastasis module [11]. Then, to further explore the relationship between histopathological imaging and genomic data, we proposed the Multi-Dimensional Constrained Joint Non-Negative Matrix Factorization (MDJNMF) [12]. This method effectively discovered the biological function modules related to sarcoma or lung metastasis and reveals the significant correlation between imaging features and genetic variation features.…”

Section: Introductionmentioning

confidence: 93%

“…Specifically, we used the overlap rate calculation method consistent with previous research [12]. The overlap rate is defined as…”

Section: Parameter Selectionmentioning

confidence: 99%

Integration of Imaging Genomics Data for the Study of Alzheimer's Disease Using Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization

2021

Self Cite

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Image genetics reveal the connection between microscopic genetics and macroscopic imaging and then detect diseases' biomarkers. In this research, we make full use of the prior knowledge that has significant reference value for exploring the correlation of brain and genetics to explore more biologically substantial biomarkers. In this paper, we propose Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization (JCB-SNMF). The algorithm simultaneously projects structural Magnetic Resonance Imaging (sMRI), Single Nucleotide Polymorphism sites (SNPs), and gene expression data onto a common feature space, where heterogeneous variables with large coefficients in the same projection direction form a common module. In addition, the connectivity information of each region of the brain and genetic data are added as prior knowledge to identify regions of interest (ROI), SNP, and gene-related risks related to Alzheimer's disease (AD) patients. GraphNet regularization increases the anti-noise performance of the algorithm and the biological interpretability of the results. The simulation results show that compared with other NMF-based algorithms (JNMF, JSNMNMF), JCB-SNMF has better anti-noise performance and can identify and predict biomarkers closely related to AD from significant modules. By constructing a proteinprotein interaction network (PPI), we identified SF3B1, RPS20, and RBM14 as potential biomarkers of AD. We also find some significant SNP-ROI and Gene-ROI pairs. Among them, two SNPs of rs4472239 and rs11918049 and three genes of KLHL8, ZC3H11A, and OSGEPL1 may have effects on the gray matter volume of multiple brain regions. This model provides a new way to further integrate multimodal impact genetic data to identify complex disease association patterns.

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Integrating multiple genomic imaging data for the study of lung metastasis in sarcomas using multi-dimensional constrained joint non-negative matrix factorization

Cited by 22 publications

References 38 publications

Integration of RNA molecules data with prior-knowledge driven Joint Deep Semi-Negative Matrix Factorization for heart failure study

Integration of RNA molecules data with prior-knowledge driven Joint Deep Semi-Negative Matrix Factorization for heart failure study

An improved joint non-negative matrix factorization for identifying surgical treatment timing of neonatal necrotizing enterocolitis

Integration of Imaging Genomics Data for the Study of Alzheimer's Disease Using Joint-Connectivity-Based Sparse Nonnegative Matrix Factorization

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