Predicting lncRNA-disease associations using network topological similarity based on deep mining heterogeneous networks

Zhang, Hui; Peng, Cheng; Han, Siyu; Du, Wei; Liu, Ying

doi:10.1016/j.mbs.2019.108229

Cited by 21 publications

(26 citation statements)

References 27 publications

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“…Secondly, we compared LGDLDA with four famous lncRNA-disease association prediction methods on a small lncRNA-disease association simulation network. Four state-of-art methods include NCPLDA [35], IDHI-MIRW [36], LncDisAP [37] and NCPHLDA [38]. Finally, LGDLDA was applied to three real cancer samples to predict potential disease-related lncRNAs.…”

Section: Resultsmentioning

confidence: 99%

“…There may be two issues to consider: (i) Does the randomness in the randomly divided sample affect the stability of the method?? (ii) Is the stability of LGDLDA better than NCPLDA [48], IDHI-MIRW [36], LncDisAP [37] and NCPHLDA [38] ? To address the two issues, we observed the performance of the method in two experiments.…”

Section: Comparison Of Methods Stabilitymentioning

confidence: 99%

“…LncDisAP [37] and IDHI-MIRW [36] are prediction methods based on multiple biological datasets and RWR algorithm. NCPHLDA [38] and NCPLDA [35] are network-based methods. We performed these experiments on a computer with an Intel i9-10900X CPU and 512RAM.…”

Section: Comparison With Four State-of-art Methods On a Small Simulatmentioning

confidence: 99%

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A Machine Learning Framework that Integrates Multi-omics Data Predicts Cancer-related LncRNAs

Yuan

Zhao

Sun

et al. 2021

Preprint

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Background: LncRNAs (Long non-coding RNAs) are a type of non-coding RNA molecule with transcript length longer than 200 nucleotides. LncRNA has been novel candidate biomarkers in cancer diagnosis and prognosis. However, it is difficult to discover the true association mechanism between lncRNAs and complex diseases. The unprecedented enrichment of multi-omics data and the rapid development of machine learning technology provide us with the opportunity to design a machine learning framework to study the relationship between lncRNAs and complex diseases. Results: In this article, we proposed a new machine learning approach, namely LGDLDA (LncRNA-Gene-Disease association networks based LncRNA-Disease Association prediction), for disease-related lncRNAs association prediction based multi-omics data, machine learning methods and neural network neighborhood information aggregation. Firstly, LGDLDA calculates the similarity matrix of lncRNA, gene and disease respectively. LGDLDA calculates the similarity between lncRNAs through the lncRNA expression profile matrix, lncRNA-miRNA interaction matrix and lncRNA-protein interaction matrix. LGDLDA obtains gene similarity matrix by calculating the lncRNA-gene association matrix and the gene-disease association matrix. LGDLDA obtains disease similarity matrix by calculating the disease ontology, the disease-miRNA association matrix, and Gaussian interaction profile kernel similarity. Secondly, LGDLDA integrates the neighborhood information in similarity matrices by using nonlinear feature learning of neural network. Thirdly, LGDLDA uses embedded node representations to approximate the observed matrices. Finally, LGDLDA ranks candidate lncRNA-disease pairs and then selects potential disease-related lncRNAs. Conclusions: Compared with lncRNA-disease prediction methods, IHI-BMLLR takes into account more critical information and obtains the performance improvement cancer-related lncRNA predictions. Randomly split data experiment results show that the stability of LGDLDA is better than IDHI-MIRW, NCPLDA, LncDisAP and NCPHLDA. The results on different simulation data sets show that LGDLDA can accurately and effectively predict the disease-related lncRNAs. Furthermore, we applied LGDLDA to three real cancer data including gastric cancer, colorectal cancer and breast cancer to predict potential cancer-related lncRNAs.

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Section: Resultsmentioning

confidence: 99%

Section: Comparison Of Methods Stabilitymentioning

confidence: 99%

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A Machine Learning Framework that Integrates Multi-omics Data Predicts Cancer-related LncRNAs

Yuan

Zhao

Sun

et al. 2021

Preprint

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“…and recommendation system ideas was applied in three models to predict potential lncRNAdisease associations, including LDASR, ECLDA, and weighted bagging LightGBM model. [49][50][51] Three methods (CNNLDA, CNNDLP, and GCNLDA) were developed by Xuan et al [52][53][54] to construct the final module through the integration of the convolutional module and attention module. LDAPred, proposed by Xuan et al, 55 introduced the convolutional neural network based on the integration of resource allocation and matrix completion.…”

Section: Multi-model Integration-based Methodsmentioning

confidence: 99%

Computational Methods and Applications for Identifying Disease-Associated lncRNAs as Potential Biomarkers and Therapeutic Targets

Yan

Zhang

Bao

et al. 2020

Molecular Therapy - Nucleic Acids

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Long non-coding RNAs (lncRNAs) have been recognized as critical components of a broad genomic regulatory network and play pivotal roles in physiological and pathological processes. Identification of disease-associated lncRNAs is becoming increasingly crucial for fundamentally improving our understanding of molecular mechanisms of disease and developing novel biomarkers and therapeutic targets. Considering lower efficiency and higher time and labor cost of biological experiments, computer-aided inference of disease-associated RNAs has become a promising avenue for facilitating the study of lncRNA functions and provides complementary value for experimental studies. In this study, we first summarize data and knowledge resources publicly available for the study of lncRNA-disease associations. Then, we present an updated systematic overview of dozens of computational methods and models for inferring lncRNA-disease associations proposed in recent years. Finally, we explore the perspectives and challenges for further studies. Our study provides a guide for biologists and medical scientists to look for dedicated resources and more competent tools for accelerating the unraveling of disease-associated lncRNAs.

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“…Sumathipala et al developed a network diffusion based LDA prediction model by integrating the proteindisease, protein-lncRNA and protein-protein associations [22]. Zhang et al developed a DeepWalk based LDA prediction model by integrating the miRNA-disease, lncRNAdisease, and miRNA-lncRNA associations [23]. Xie et al implemented a similarity kernel fusion based LDA prediction model (SFK-LDA) by fusing the DSS and cosine similarity, and the lncRNA expression similarity and cosine similarity [24].…”

Section: Introductionmentioning

confidence: 99%

A random forest based computational model for predicting novel lncRNA-disease associations

Yao

Zhan

et al. 2020

BMC Bioinformatics

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Background: Accumulated evidence shows that the abnormal regulation of long non-coding RNA (lncRNA) is associated with various human diseases. Accurately identifying disease-associated lncRNAs is helpful to study the mechanism of lncRNAs in diseases and explore new therapies of diseases. Many lncRNA-disease association (LDA) prediction models have been implemented by integrating multiple kinds of data resources. However, most of the existing models ignore the interference of noisy and redundancy information among these data resources. Results: To improve the ability of LDA prediction models, we implemented a random forest and feature selection based LDA prediction model (RFLDA in short). First, the RFLDA integrates the experiment-supported miRNA-disease associations (MDAs) and LDAs, the disease semantic similarity (DSS), the lncRNA functional similarity (LFS) and the lncRNA-miRNA interactions (LMI) as input features. Then, the RFLDA chooses the most useful features to train prediction model by feature selection based on the random forest variable importance score that takes into account not only the effect of individual feature on prediction results but also the joint effects of multiple features on prediction results. Finally, a random forest regression model is trained to score potential lncRNA-disease associations. In terms of the area under the receiver operating characteristic curve (AUC) of 0.976 and the area under the precision-recall curve (AUPR) of 0.779 under 5-fold cross-validation, the performance of the RFLDA is better than several state-of-the-art LDA prediction models. Moreover, case studies on three cancers demonstrate that 43 of the 45 lncRNAs predicted by the RFLDA are validated by experimental data, and the other two predicted lncRNAs are supported by other LDA prediction models. Conclusions: Cross-validation and case studies indicate that the RFLDA has excellent ability to identify potential disease-associated lncRNAs.

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Predicting lncRNA-disease associations using network topological similarity based on deep mining heterogeneous networks

Cited by 21 publications

References 27 publications

A Machine Learning Framework that Integrates Multi-omics Data Predicts Cancer-related LncRNAs

A Machine Learning Framework that Integrates Multi-omics Data Predicts Cancer-related LncRNAs

Computational Methods and Applications for Identifying Disease-Associated lncRNAs as Potential Biomarkers and Therapeutic Targets

A random forest based computational model for predicting novel lncRNA-disease associations

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