NSRGRN: a network structure refinement method for gene regulatory network inference

Liu, Wei; Yu, Yang; Xu, Lisha; Fu, Xiquan; Sun, Ruiqing; Yang, Li

doi:10.1093/bib/bbad129

Cited by 13 publications

(5 citation statements)

References 59 publications

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“…Mathematical models, including graph-based convolutional neural networks (GCN) and conditional random fields (CRF), predict human lncRNA-miRNA interactions 30 . The MPCLCDA model effectively predicts circRNA–disease associations by using automatically selected meta-path and contrastive learning 31 , while the network structure refinement method for gene regulatory networks (NSRGRN) model optimally elucidates gene regulatory networks 32 . However, it's important to acknowledge the limitations of this study: Our GWAS data were derived solely from European populations, excluding other ethnic groups.…”

Section: Discussionmentioning

confidence: 99%

Gamma-glutamyl transferase and calculus of kidney incidence: a Mendelian randomization study

Li,

Pang,

et al. 2023

Sci Rep

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Elevated Gamma-glutamyl transferase (GGT) levels are often suggestive of cholelithiasis, and previous studies have indicated that GGT is highly expressed in the urinary system. Therefore, we hypothesized that there may be an association between GGT levels and calculus of kidney (CK) incidence. To investigate this potential causal relationship, we employed Mendelian randomization (MR) analysis. Additionally, we analyzed the levels of other liver enzymes, including alanine transaminase (ALT) and alkaline phosphatase (ALP). The relationship between GGT levels and CK incidence was analyzed using two-sample Mendelian randomization. Summary Genome-Wide Association Studies data were utilized for this analysis. 33 single nucleotide polymorphisms known to be associated with GGT levels were employed as instrumental variables. We employed several MR methods including IVW (inverse variance weighting), MR-Egger, weighted median, weighted mode, and MR-PRESSO (Mendelian Randomization Pleiotropy RESidual Sum and Outlier). Furthermore, we conducted tests for horizontal multivariate validity, heterogeneity, and performed leave-one-out analysis to ensure the stability of the results. Overall, several MR methods yielded statistically significant results with a p-value < 0.05. The results from the IVW analysis yielded an odds ratio (OR) of 1.0062 with a 95% confidence interval (CI) of 1.0016–1.0109 (p = 0.0077). Additional MR methods provided supplementary results: MR-Egger (OR 1.0167, 95% CI 1.0070–1.0266, p = 0.0040); weighted median (OR 1.0058, 95% CI 1.0002–1.0115, p = 0.0423); and weighted mode (OR 1.0083, 95% CI 1.0020–1.0146, p- = 0.0188). Sensitivity analyses did not reveal heterogeneity or outliers. Although potential horizontal pleiotropy emerged, we speculate that this could be attributed to inadequate test efficacy. However, subsequent use of MR-PRESSO did not provide evidence of pleiotropy. Our analysis suggests a positive association between elevated GGT levels and CK incidence, indicating an increased risk of CK development. However, no causal relationship was observed between levels of ALP or ALT and CK incidence.

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Section: Discussionmentioning

confidence: 99%

Gamma-glutamyl transferase and calculus of kidney incidence: a Mendelian randomization study

Li,

Pang,

et al. 2023

Sci Rep

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“…In our future research, we aim to explore the implementation of graph neural networks (GNNs) 65 to enhance the construction of specific ceRNA networks in the third step of the pipeline. GNNs have shown promise in improving the accuracy of network analysis and prediction, and we anticipate that leveraging this technology will lead to the discovery of more effective biomarkers.…”

Section: Discussionmentioning

confidence: 99%

Exploring ceRNA networks for key biomarkers in breast cancer subtypes and immune regulation

Shariatmadar Taleghani,

Zohrab Beigi,

Zare-Mirakabad

et al. 2023

Sci Rep

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Breast cancer is a major global health concern, and recent researches have highlighted the critical roles of non-coding RNAs in both cancer and the immune system. The competing endogenous RNA hypothesis suggests that various types of RNA, including coding and non-coding RNAs, compete for microRNA targets, acting as molecular sponges. This study introduces the Pre_CLM_BCS pipeline to investigate the potential of long non-coding RNAs and circular RNAs as biomarkers in breast cancer subtypes. The pipeline identifies specific modules within each subtype that contain at least one long non-coding RNA or circular RNA exhibiting significantly distinct expression patterns when compared to other subtypes. The results reveal potential biomarker genes for each subtype, such as circ_001845, circ_001124, circ_003925, circ_000736, and circ_003996 for the basal-like subtype, circ_00306 and circ_00128 for the luminal B subtype, circ_000709 and NPHS1 for the normal-like subtype, CAMKV and circ_001855 for the luminal A subtype, and circ_00128 and circ_00173 for the HER2+ subtype. Additionally, certain long non-coding RNAs and circular RNAs, including RGS5-AS1, C6orf223, HHLA3-AS1, circ_000349, circ_003996, circ_003925, circ_002665, circ_001855, and DLEU1, are identified as potential regulators of T cell mechanisms, underscoring their importance in understanding breast cancer progression in various subtypes. This pipeline provides valuable insights into cancer and immune-related processes in breast cancer subtypes.

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“…With the increasing availability of large datasets, it is possible to predict the relationship between small molecule drugs and miRNAs and use this information to improve the efficacy and safety of drugs ( Wang et al, 2019 ; Chen et al, 2020 ). This field has tremendous potential in discovering new therapeutic targets and developing personalized drugs ( Chen et al, 2021 ; Liu et al, 2023 ; Xu et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Prediction of small molecule drug-miRNA associations based on GNNs and CNNs

Niu,

Gao,

Xia

et al. 2023

Front. Genet.

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MicroRNAs (miRNAs) play a crucial role in various biological processes and human diseases, and are considered as therapeutic targets for small molecules (SMs). Due to the time-consuming and expensive biological experiments required to validate SM-miRNA associations, there is an urgent need to develop new computational models to predict novel SM-miRNA associations. The rapid development of end-to-end deep learning models and the introduction of ensemble learning ideas provide us with new solutions. Based on the idea of ensemble learning, we integrate graph neural networks (GNNs) and convolutional neural networks (CNNs) to propose a miRNA and small molecule association prediction model (GCNNMMA). Firstly, we use GNNs to effectively learn the molecular structure graph data of small molecule drugs, while using CNNs to learn the sequence data of miRNAs. Secondly, since the black-box effect of deep learning models makes them difficult to analyze and interpret, we introduce attention mechanisms to address this issue. Finally, the neural attention mechanism allows the CNNs model to learn the sequence data of miRNAs to determine the weight of sub-sequences in miRNAs, and then predict the association between miRNAs and small molecule drugs. To evaluate the effectiveness of GCNNMMA, we implement two different cross-validation (CV) methods based on two different datasets. Experimental results show that the cross-validation results of GCNNMMA on both datasets are better than those of other comparison models. In a case study, Fluorouracil was found to be associated with five different miRNAs in the top 10 predicted associations, and published experimental literature confirmed that Fluorouracil is a metabolic inhibitor used to treat liver cancer, breast cancer, and other tumors. Therefore, GCNNMMA is an effective tool for mining the relationship between small molecule drugs and miRNAs relevant to diseases.

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NSRGRN: a network structure refinement method for gene regulatory network inference

Cited by 13 publications

References 59 publications

Gamma-glutamyl transferase and calculus of kidney incidence: a Mendelian randomization study

Gamma-glutamyl transferase and calculus of kidney incidence: a Mendelian randomization study

Exploring ceRNA networks for key biomarkers in breast cancer subtypes and immune regulation

Prediction of small molecule drug-miRNA associations based on GNNs and CNNs

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