HBRWRLDA: predicting potential lncRNA–disease associations based on hypergraph bi-random walk with restart

Xie, Guobo; Zhu, Yinting; Lin, Zhiyi; Sun, Yeping; Gu, Guosheng; Li, Jianming; Wang, Weiming

doi:10.1007/s00438-022-01909-y

Cited by 4 publications

(2 citation statements)

References 59 publications

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“…It not only has a good performance, but also has good expansibility. For example, metabolite-disease association prediction 91 , miRNA-disease association prediction 92 , lncRNA-disease association prediction 93 , and lncRNA-protein association prediction 94 .…”

Section: Discussionmentioning

confidence: 99%

JSCSNCP-LMA: a method for predicting the association of lncRNA–miRNA

Wang

Zheng

et al. 2022

Sci Rep

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Non-coding RNAs (ncRNAs) have long been considered the "white elephant" on the genome because they lack the ability to encode proteins. However, in recent years, more and more biological experiments and clinical reports have proved that ncRNAs account for a large proportion in organisms. At the same time, they play a decisive role in the biological processes such as gene expression and cell growth and development. Recently, it has been found that short sequence non-coding RNA(miRNA) and long sequence non-coding RNA(lncRNA) can regulate each other, which plays an important role in various complex human diseases. In this paper, we used a new method (JSCSNCP-LMA) to predict lncRNA–miRNA with unknown associations. This method combined Jaccard similarity algorithm, self-tuning spectral clustering similarity algorithm, cosine similarity algorithm and known lncRNA–miRNA association networks, and used the consistency projection to complete the final prediction. The results showed that the AUC values of JSCSNCP-LMA in fivefold cross validation (fivefold CV) and leave-one-out cross validation (LOOCV) were 0.9145 and 0.9268, respectively. Compared with other models, we have successfully proved its superiority and good extensibility. Meanwhile, the model also used three different lncRNA–miRNA datasets in the fivefold CV experiment and obtained good results with AUC values of 0.9145, 0.9662 and 0.9505, respectively. Therefore, JSCSNCP-LMA will help to predict the associations between lncRNA and miRNA.

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Section: Discussionmentioning

confidence: 99%

JSCSNCP-LMA: a method for predicting the association of lncRNA–miRNA

Wang

Zheng

et al. 2022

Sci Rep

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“…This type of method first constructs heterogeneous networks of lncRNAs and diseases and then identifies LDAs via matrix decomposition, random walk, and so on. To predict potential LDAs, LRWRHLDA combined Laplace normalized random walk with restart ( Wang et al, 2022 ), LDGRNMF used graph regularized nonnegative matrix factorization ( Wang et al, 2021 ), DSCMF developed a dual sparse collaborative matrix factorization approach ( Liu et al, 2021a ), RWSF-BLP added random walk-based multi-similarity fusion to bidirectional label propagation ( Xie et al, 2021 ), HBRWRLDA utilized bi-random walk on hypergraphs ( Xie et al, 2022 ), and MHRWRLDA exploited a random walk model with restart through multiplex and heterogeneous networks ( Yao et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting potential lncRNA biomarkers for lung cancer and neuroblastoma based on an ensemble of a deep neural network and LightGBM

Su,

Lu,

et al. 2023

Front. Genet.

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Introduction: Lung cancer is one of the most frequent neoplasms worldwide with approximately 2.2 million new cases and 1.8 million deaths each year. The expression levels of programmed death ligand-1 (PDL1) demonstrate a complex association with lung cancer. Neuroblastoma is a high-risk malignant tumor and is mainly involved in childhood patients. Identification of new biomarkers for these two diseases can significantly promote their diagnosis and therapy. However, in vivo experiments to discover potential biomarkers are costly and laborious. Consequently, artificial intelligence technologies, especially machine learning methods, provide a powerful avenue to find new biomarkers for various diseases.Methods: We developed a machine learning-based method named LDAenDL to detect potential long noncoding RNA (lncRNA) biomarkers for lung cancer and neuroblastoma using an ensemble of a deep neural network and LightGBM. LDAenDL first computes the Gaussian kernel similarity and functional similarity of lncRNAs and the Gaussian kernel similarity and semantic similarity of diseases to obtain their similar networks. Next, LDAenDL combines a graph convolutional network, graph attention network, and convolutional neural network to learn the biological features of the lncRNAs and diseases based on their similarity networks. Third, these features are concatenated and fed to an ensemble model composed of a deep neural network and LightGBM to find new lncRNA–disease associations (LDAs). Finally, the proposed LDAenDL method is applied to identify possible lncRNA biomarkers associated with lung cancer and neuroblastoma.Results: The experimental results show that LDAenDL computed the best AUCs of 0.8701, 107 0.8953, and 0.9110 under cross-validation on lncRNAs, diseases, and lncRNA‐disease pairs on Dataset 1, respectively, and 0.9490, 0.9157, and 0.9708 on Dataset 2, respectively. Furthermore, AUPRs of 0.8903, 0.9061, and 0.9166 under three cross‐validations were obtained on Dataset 1, and 0.9582, 0.9122, and 0.9743 on Dataset 2. The results demonstrate that LDAenDL significantly outperformed the other four classical LDA prediction methods (i.e., SDLDA, LDNFSGB, IPCAF, and LDASR). Case studies demonstrate that CCDC26 and IFNG-AS1 may be new biomarkers of lung cancer, SNHG3 may associate with PDL1 for lung cancer, and HOTAIR and BDNF-AS may be potential biomarkers of neuroblastoma.Conclusion: We hope that the proposed LDAenDL method can help the development of targeted therapies for these two diseases.

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