Co-differential Gene Selection and Clustering Based on Graph Regularized Multi-View NMF in Cancer Genomic Data

Yu, Na; Gao, Ying-Lian; Liu, Jin-Xing; Shang, Junliang; Zhu, Rong; Dai, Lingyun

doi:10.3390/genes9120586

Cited by 28 publications

(17 citation statements)

References 26 publications

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“…Identification of molecular types associated with metabolic genes. Nonnegative matrix factorization (NMF) is an unsupervised clustering method widely used in identifying genomics-based tumor molecular subtypes (35,36). To further determine the association between expression levels of energy metabolism genes and phenotypes, the NMF method was used to cluster samples according to the expression profiles of energy metabolism-related genes associated with ovarian cancer prognosis, and standard 'brunet' for 50 iterations was selected by NMF.…”

Section: Methodsmentioning

confidence: 99%

Identification of an energy metabolism‑related gene signature in ovarian cancer prognosis

Wang

2020

Oncol Rep

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Changes in energy metabolism may be potential biomarkers and therapeutic targets for cancer as they frequently occur within cancer cells. However, basic cancer research has failed to reach a consistent conclusion on the function(s) of mitochondria in energy metabolism. The significance of energy metabolism in the prognosis of ovarian cancer remains unclear; thus, there remains an urgent need to systematically analyze the characteristics and clinical value of energy metabolism in ovarian cancer. Based on gene expression patterns, the present study aimed to analyze energy metabolism-associated characteristics to evaluate the prognosis of patients with ovarian cancer. A total of 39 energy metabolism-related genes significantly associated with prognosis were obtained, and three molecular subtypes were identified by nonnegative matrix factorization clustering, among which the C1 subtype was associated with poor clinical outcomes of ovarian cancer. The immune response was enhanced in the tumor microenvironment. A total of 888 differentially expressed genes were identified in C1 compared with the other subtypes, and the results of the pathway enrichment analysis demonstrated that they were enriched in the 'PI3K-Akt signaling pathway', 'cAMP signaling pathway', 'ECM-receptor interaction' and other pathways associated with the development and progression of tumors. Finally, eight characteristic genes (tolloid-like 1 gene, type XVI collagen, prostaglandin F2α, cartilage intermediate layer protein 2, kinesin family member 26b, interferon inducible protein 27, growth arrest-specific gene 1 and chemokine receptor 7) were obtained through LASSO feature selection; and a number of them have been demonstrated to be associated with ovarian cancer progression. In addition, Cox regression analysis was performed to establish an 8-gene signature, which was determined to be an independent prognostic factor for patients with ovarian cancer and could stratify sample risk in the training, test and external validation datasets (P<0.01; AUC >0.8). Gene Set Enrichment Analysis results revealed that the 8-gene signature was involved in important biological processes and pathways of ovarian cancer. In conclusion, the present study established an 8-gene signature associated with metabolic genes, which may provide new insights into the effects of energy metabolism on ovarian cancer. The 8-gene signature may serve as an independent prognostic factor for ovarian cancer patients.

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Section: Methodsmentioning

confidence: 99%

Identification of an energy metabolism‑related gene signature in ovarian cancer prognosis

Wang

2020

Oncol Rep

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“…Multiview NMF is also used for the selection of common codifferential genes [46]. In this paper, the authors implemented a graph-regularized version of multiview NMF (GMvNMF) to encode the data manifold of genomic data.…”

Section: Cancersmentioning

confidence: 99%

Multiview learning for understanding functional multiomics

Nguyen

Wang

2020

PLoS Comput Biol

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The molecular mechanisms and functions in complex biological systems currently remain elusive. Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification of biological functions and mechanisms via multiple facets. However, integrating these large-scale multiomics data and discovering functional insights are, nevertheless, challenging tasks. To address these challenges, machine learning has been broadly applied to analyze multiomics. This review introduces multiview learning-an emerging machine learning field-and envisions its potentially powerful applications to multiomics. In particular, multiview learning is more effective than previous integrative methods for learning data's heterogeneity and revealing cross-talk patterns. Although it has been applied to various contexts, such as computer vision and speech recognition, multiview learning has not yet been widely applied to biological data-specifically, multiomics data. Therefore, this paper firstly reviews recent multiview learning methods and unifies them in a framework called multiview empirical risk minimization (MV-ERM). We further discuss the potential applications of each method to multiomics, including genomics, transcriptomics, and epigenomics, in an aim to discover the functional and mechanistic interpretations across omics. Secondly, we explore possible applications to different biological systems, including human diseases (e.g., brain disorders and cancers), plants, and single-cell analysis, and discuss both the benefits and caveats of using multiview learning to discover the molecular mechanisms and functions of these systems.

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“…Graph regularization framework [39] has been widely used in semi-supervised learning [13] and unsupervised learning [40][41][42][43]. In the process of data processing, the graph regularization can preserve the local manifold structure between data, so that the structural information can be extracted, which is beneficial to clustering or classification problems.…”

Section: Graph Regularizationmentioning

confidence: 99%

Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

et al. 2020

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Background As a machine learning method with high performance and excellent generalization ability, extreme learning machine (ELM) is gaining popularity in various studies. Various ELM-based methods for different fields have been proposed. However, the robustness to noise and outliers is always the main problem affecting the performance of ELM. Results In this paper, an integrated method named correntropy induced loss based sparse robust graph regularized extreme learning machine (CSRGELM) is proposed. The introduction of correntropy induced loss improves the robustness of ELM and weakens the negative effects of noise and outliers. By using the L2,1-norm to constrain the output weight matrix, we tend to obtain a sparse output weight matrix to construct a simpler single hidden layer feedforward neural network model. By introducing the graph regularization to preserve the local structural information of the data, the classification performance of the new method is further improved. Besides, we design an iterative optimization method based on the idea of half quadratic optimization to solve the non-convex problem of CSRGELM. Conclusions The classification results on the benchmark dataset show that CSRGELM can obtain better classification results compared with other methods. More importantly, we also apply the new method to the classification problems of cancer samples and get a good classification effect.

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Co-differential Gene Selection and Clustering Based on Graph Regularized Multi-View NMF in Cancer Genomic Data

Cited by 28 publications

References 26 publications

Identification of an energy metabolism‑related gene signature in ovarian cancer prognosis

Identification of an energy metabolism‑related gene signature in ovarian cancer prognosis

Multiview learning for understanding functional multiomics

Correntropy induced loss based sparse robust graph regularized extreme learning machine for cancer classification

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