iCircDA-MF: identification of circRNA-disease associations based on matrix factorization

Wei, Hang; Liu, Bin

doi:10.1093/bib/bbz057

Cited by 115 publications

(48 citation statements)

References 67 publications

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“…Nonnegative matrix factorization (NMF) is a significant algorithm which can be applied in recommender system for data representation [32]. Recent years, NMF has been successfully utilized to predict potential interactions of drug-drug [41], lncRNA-protein [42], miRNA-disease [43], Microbe-disease [44], drug-disease [45], CircRNA-disease [46], etc.. NMF aims to obtain lncRNA-miRNA interaction score matrix by decomposing the original adjacency matrix into two low-dimensional nonnegative matrices directly [38]. In this work, the lncRNA-miRNA interaction adjacency matrix Y ∈ R 468×262 is decomposed into U ∈ R k×468 and V ∈ R k×262 , k is the sub-space dimensionality (k < rn/(r + n)).…”

Section: B Related Work 1) the Standard Nonnegative Matrix Factorizamentioning

confidence: 99%

GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions

Wang

You

et al. 2020

IEEE Access

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Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been involved in various biological processes. Emerging evidence suggests that the interactions between lncRNAs and miRNAs play an important role in the regulation of genes and the development of many diseases. Due to the limited scale of known lncRNA-miRNA interactions, and expensive time and labor costs for identifying them by biological experiments, more accurate and efficient lncRNA-miRNA interaction computational prediction approach urgently need to be developed. In this work, we proposed a novel computational model, GNMFLMI, to predict lncRNA-miRNA interactions using graph regularized nonnegative matrix factorization. More specifically, the similarities both lncRNA and miRNA are calculated based on known interaction information and their sequence information. Then, the affinity graphs for lncRNAs and miRNAs are constructed using the p-nearest neighbors, respectively. Finally, a graph regularized nonnegative matrix factorization model is developed to accurately infer potential interactions between lncRNAs and miRNAs. To assess the performance of GNMFLMI, five-fold cross-validation experiments are carried out. The AUC values achieved by GNMFLMI on two datasets are 0.9769 and 0.8894, respectively, which outperform the compared methods. In the case studies for lncRNA nonhsat159254.1 and miRNA hsa-mir-544a, 20 and 16 of the top-20 associations predicted by GNMFLMI are confirmed, respectively. Rigorous experimental results demonstrate that GNMFLMI can effectively predict novel lncRNA-miRNA interactions, which can provide guidance for relevant biomedical research.

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Section: B Related Work 1) the Standard Nonnegative Matrix Factorizamentioning

confidence: 99%

GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions

Wang

You

et al. 2020

IEEE Access

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“…They are mainly located in the cytoplasm or stored in exosomes, are unaffected by RNA exonucleases, are more stably expressed and less susceptible to degradation, and have been shown to exist in a wide variety of eukaryotic organisms Pradeep et al, 2020). The widespread existence of circRNAs suggests that they have certain biological functions as lncRNAs and microRNAs (miRNAs) play (Jiang et al, 2009(Jiang et al, , 2014(Jiang et al, , 2015Wang et al, 2014;Cheng L. et al, 2019;Liang et al, 2019;Wei and Liu, 2020;Yang et al, 2020). In recent years, studies have shown a diversity of formation mechanisms and biological functions of circRNAs.…”

Section: Introductionmentioning

confidence: 99%

Advances in the Identification of Circular RNAs and Research Into circRNAs in Human Diseases

Jiao

Huang

et al. 2021

Front. Genet.

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Circular RNAs (circRNAs) are a class of endogenous non-coding RNAs (ncRNAs) with a closed-loop structure that are mainly produced by variable processing of precursor mRNAs (pre-mRNAs). They are widely present in all eukaryotes and are very stable. Currently, circRNA studies have become a hotspot in RNA research. It has been reported that circRNAs constitute a significant proportion of transcript expression, and some are significantly more abundantly expressed than other transcripts. CircRNAs have regulatory roles in gene expression and critical biological functions in the development of organisms, such as acting as microRNA sponges or as endogenous RNAs and biomarkers. As such, they may have useful functions in the diagnosis and treatment of diseases. CircRNAs have been found to play an important role in the development of several diseases, including atherosclerosis, neurological disorders, diabetes, and cancer. In this paper, we review the status of circRNA research, describe circRNA-related databases and the identification of circRNAs, discuss the role of circRNAs in human diseases such as colon cancer, atherosclerosis, and gastric cancer, and identify remaining research questions related to circRNAs.

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“…Circular RNAs (circRNAs), non-coding RNA molecules with closed loop structures, play important roles in diverse human diseases, including cancers (Liu, Li, Luo, & Zhu, 2019;Wei & Liu, 2019). For instance, circDDX17 acts as a tumor suppressor by suppressing proliferation and accelerating apoptosis in colorectal cancer cells (Xiao et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

CircACAP2 promotes cell proliferation and migration in lung adenocarcinoma via LASP1-mediated TGF‐β/Smad3 pathway

Xu¹,

Ma²,

Guan³

et al. 2021

Preprint

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Lung adenocarcinoma (LUAD), a common malignant tumor, has led to a great number of deaths around the world. Circular RNAs (circRNAs) have been certified as essential players in the progression of diverse cancers. CircRNA ACAP2 (hsa_circ_0068568) is an oncogene in several cancers. However, the role of circACAP2 in LUAD remains unknown. This study revealed that the expression of circACAP2 was significantly elevated in LUAD tissues and cell lines, especially in the tissues of LUAD patients at advanced stage. Additionally, circACAP2 enhanced cell proliferation, migration, invasion abilities and epithelial-mesenchymal transition (EMT) process in LUAD. Moreover, miR-342-3p interacted with circACAP2 in LUAD cells. Importantly, we found that miR-342-3p targeted LIM and SH3 protein 1 (LASP1), and circACAP2 positively regulated LASP1 expression by competing for miR-342-3p in LUAD. Further, it was confirmed that circACAP2 promoted the malignant behaviors and stimulated the activation of TGF-β/Smad3 pathway in LUAD by modulating the miR-342-3p/LASP1 axis. To conclude, the molecular regulatory mechanism of circACAP2 in LUAD was under discussion in the current study. The findings revealed that circACAP2 facilitated malignant phenotypes in LUAD via the activation of the TGF‐β/Smad3 pathway.

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iCircDA-MF: identification of circRNA-disease associations based on matrix factorization

Cited by 115 publications

References 67 publications

GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions

GNMFLMI: Graph Regularized Nonnegative Matrix Factorization for Predicting LncRNA-MiRNA Interactions

Advances in the Identification of Circular RNAs and Research Into circRNAs in Human Diseases

CircACAP2 promotes cell proliferation and migration in lung adenocarcinoma via LASP1-mediated TGF‐β/Smad3 pathway

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