Hyper-Graph Regularized Constrained NMF for Selecting Differentially Expressed Genes and Tumor Classification

Jiao, Cui-Na; Gao, Ying-Lian; Yu, Na; Liu, Jin-Xing; Qi, Lianyong

doi:10.1109/jbhi.2020.2975199

Cited by 38 publications

(10 citation statements)

References 38 publications

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“…In bioinformatics, matrix factorization method has a wide range of applications, such as clustering and feature selection [13], lncRNA-disease associations prediction [14], tumor classification [15] and marker extraction [16]. In DTI prediction, the known DTI network is usually represented by a matrix.…”

Section: Introductionmentioning

confidence: 99%

NNDSVD-GRMF: A Graph Dual Regularization Matrix Factorization Method Using Non-Negative Initialization for Predicting Drug-Target Interactions

Zhang

Xie

2022

IEEE Access

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Accurate drug-target interactions (DTIs) prediction can significantly speed up the process of new drug design and development. Recently, many matrix factorization methods have been used to predict DTIs. However, most of them use heuristic and iterative strategies, and their convergence and performance can not be guaranteed. In order to accurately predict DTIs, we propose a new algorithm, NNDSVD-GRMF, our method is based on graph dual regularization non-negative matrix factorization (GDNMF) and nonnegative double singular value decomposition (NNDSVD), which considers both the initialization stage of the non-negative matrix factorization and the structural information of the data and features. At the same time, in order to improve the adaptability of the algorithm, the extension of the NNDSVD-GRMF (NNDSVD-WGRMF) is also proposed. Extensive experimental results show that our methods have better performance than other state-of-the-art methods. In the case studies, among the 10 highest-scoring drugs predicted to interact with androgen receptor, 9 drugs have been validated, and among the 10 highest-scoring target proteins predicted to be targeted by the drug nicotine bitartrate, 9 targets have been validated.

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Section: Introductionmentioning

confidence: 99%

NNDSVD-GRMF: A Graph Dual Regularization Matrix Factorization Method Using Non-Negative Initialization for Predicting Drug-Target Interactions

Zhang

Xie

2022

IEEE Access

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“…With the pervasion of second-generation sequencing, it is currently a prevalent cancer research method that analyzing high-throughput expression data of cancer patients through bioinformatics methods. A study constructed a tumor classifier for early tumor diagnosis with machine learning algorithms (Jiao et al, 2020). Another study established a risk prognostic model to conduct risk prediction for patients, beneficial for clinicians to make personalized diagnosis and treatment (Zhang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Exploration of Prognostic Biomarkers for Lung Adenocarcinoma Through Bioinformatics Analysis

Zeng

et al. 2021

Front. Genet.

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With the development of computer technology, screening cancer biomarkers based on public databases has become a common research method. Here, an eight-gene prognostic model, which could be used to judge the prognosis of patients with lung adenocarcinoma (LUAD), was developed through bioinformatics methods. This study firstly used several gene datasets from GEO database to mine differentially expressed genes (DEGs) in LUAD tissue and healthy tissue via joint analysis. Later, enrichment analysis for the DEGs was performed, and it was found that the DEGs were mainly activated in pathways involved in extracellular matrix, cell adhesion, and leukocyte migration. Afterward, a TCGA cohort was used to perform univariate Cox, least absolute shrinkage and selection operator method, and multivariate Cox regression analyses for the DEGs, and a prognostic model consisting of eight genes (GPX3, TCN1, ASPM, PCP4, CAV2, S100P, COL1A1, and SPOK2) was established. Receiver operation characteristic (ROC) curve was then used to substantiate the diagnostic efficacy of the prognostic model. The survival significance of signature genes was verified through the GEPIA database, and the results exhibited that the risk coefficients of the eight genes were basically congruous with the effects of these genes on the prognosis in the GEPIA database, which suggested that the results were accurate. Finally, combined with clinical characteristics of patients, the diagnostic independence of the prognostic model was further validated through univariate and multivariate regression, and the results indicated that the model had independent prognostic value. The overall finding of the study manifested that the eight-gene prognostic model is closely related to the prognosis of LUAD patients, and can be used as an independent prognostic indicator. Additionally, the prognostic model in this study can help doctors make a better diagnosis in treatment and ultimately benefit LUAD patients.

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“…The experimental data X are usually obtained from a union of multiple subspaces rather than a single space, where indicates low-dimensional space hidden in high-dimensional space [ 13 – 15 ]. Since these methods related to PCA prefer to research the data obtained from a single low-dimensional space, Liu et al .…”

Section: Introductionmentioning

confidence: 99%

A truncated nuclear norm and graph-Laplacian regularized low-rank representation method for tumor clustering and gene selection

Liu

2021

BMC Bioinformatics

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Background Clustering and feature selection act major roles in many communities. As a matrix factorization, Low-Rank Representation (LRR) has attracted lots of attentions in clustering and feature selection, but sometimes its performance is frustrated when the data samples are insufficient or contain a lot of noise. Results To address this drawback, a novel LRR model named TGLRR is proposed by integrating the truncated nuclear norm with graph-Laplacian. Different from the nuclear norm minimizing all singular values, the truncated nuclear norm only minimizes some smallest singular values, which can dispel the harm of shrinkage of the leading singular values. Finally, an efficient algorithm based on Linearized Alternating Direction with Adaptive Penalty is applied to resolving the optimization problem. Conclusions The results show that the TGLRR method exceeds the existing state-of-the-art methods in aspect of tumor clustering and gene selection on integrated gene expression data.

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Hyper-Graph Regularized Constrained NMF for Selecting Differentially Expressed Genes and Tumor Classification

Cited by 38 publications

References 38 publications

NNDSVD-GRMF: A Graph Dual Regularization Matrix Factorization Method Using Non-Negative Initialization for Predicting Drug-Target Interactions

NNDSVD-GRMF: A Graph Dual Regularization Matrix Factorization Method Using Non-Negative Initialization for Predicting Drug-Target Interactions

Exploration of Prognostic Biomarkers for Lung Adenocarcinoma Through Bioinformatics Analysis

A truncated nuclear norm and graph-Laplacian regularized low-rank representation method for tumor clustering and gene selection

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