A Novel Method for LncRNA-Disease Association Prediction Based on an lncRNA-Disease Association Network

Ping, Pengyao; Wang, Lei; Kuang, Linai; Ye, Songtao; Iqbal, Muhammad Faisal Buland; Pei, Tingrui

doi:10.1109/tcbb.2018.2827373

Cited by 89 publications

(54 citation statements)

References 49 publications

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“…To show the prediction ability of the RFLDA, we compare it with several excellent LDA prediction models, such as SIMCLDA [33], Ping's method [18], MFLDA [31], LDAP [39], CNNLDA [44], and GCNLDA [45]. The AUCs and AUPRs of all LDA prediction models are shown in Table 1.…”

Section: Performance Comparison With Other Prediction Modelsmentioning

confidence: 99%

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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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Section: Performance Comparison With Other Prediction Modelsmentioning

confidence: 99%

“…Xiao et al proposed a paths of fixed lengths based LDA prediction model (BPLLDA) [17]. Ping et al inferred potential LDAs by an experimentsupported LDA network [18]. Fan et al implemented a RWR based LDA prediction model (IDHI-MIRW) by combining the positive pointwise mutual information with multiple heterogeneous information [19].…”

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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“…In this section, we will further use the receiver operating characteristic curve (ROC curve) to evaluate the performance of TGSO. Studies show that the larger the area under the ROC curve (AUC), the better the performance of the model, and if AUC=0.5, it means a random performance [55][56][57]. In the three kinds of yeast cell databases including the DIP, Krogan and GAVIN databases, the proportion of key proteins is very small, and the proportion of non-essential proteins and essential proteins is about 3 to 1.…”

Section: Validation By Precision-recall Curves and Roc Curvesmentioning

confidence: 99%

An Iteration Model for Identifying Essential Proteins by Combining Comprehensive PPI Network With Biological Information

Zhang

et al. 2020

Preprint

Self Cite

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Background: Essential proteins have great impacts on cell survival and development, and played important roles in disease analysis and new drug design. However, since it is inefficient and costly to identify essential proteins by using biological experiments, then there is an urgent need for automated and accurate detection methods. In recent years, the recognition of essential proteins in protein interaction networks (PPI) has become a research hotspot, and many computational models for predicting essential proteins have been proposed successively.Results: In order to achieve higher prediction performance, in this paper, a new prediction model called TGSO is proposed. In TGSO, a protein aggregation degree network is constructed first by adopting the node density measurement method for complex networks. And simultaneously, a protein co-expression interactive network is constructed by combining the gene expression information with the network connectivity, and a protein co-localization interaction network is constructed based on the subcellular localization data. And then, through integrating these three kinds of newly constructed networks, a comprehensive protein-protein interaction network will be obtained. Finally, based on the homology information, scores can be calculated out iteratively for different proteins, which can be utilized to estimate the importance of proteins effectively. Moreover, in order to evaluate the identification performance of TGSO, we have compared TGSO with 13 different latest competitive methods based on three kinds of yeast databases. And experimental results show that TGSO can achieve identification accuracies of 94\%, 82\% and 72\% out of the top 1\%, 5\% and 10\% candidate proteins respectively, which are to some degree superior to these state-of-the-art competitive models. Conclusion: We constructed a comprehensive interactive network based on multi-source data to reduce the noise and errors in the initial PPI, and combined with iterative methods to improve the accuracy of necessary protein prediction, and means that TGSO may be conducive to the future development of essential protein recognition as well.

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“…lncRNAs play critical roles in various human biological processes, such as chromatin modification, cell differentiation, proliferation and apoptosis, translational and post-translational regulation. Moreover, the abnormally expressed lncRNAs are involved in the occurrence and development of a variety of human diseases [27]. In recent years, more and more evidence has shown that lncRNAs are abnormally expressed in ovarian cumulus cells and/or GCs of PCOS patients [9,28,29].…”

Section: Lncrnasmentioning

confidence: 99%

Roles of ncRNAs in Ovarian Dysfunction of Polycystic Ovary Syndrome

Han¹,

Yu²,

Chen³

et al. 2020

Polycystic Ovarian Syndrome

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Polycystic ovary syndrome (PCOS) is a common endocrine disease in women of childbearing age. Many heterogeneous clinical manifestations of PCOS, including hyperandrogenism, obesity, insulin resistance, hirsutism, acne, chronic anovulation and infertility, seriously affected the quality of life of women worldwide and made it difficult to clearly demonstrate the specific pathophysiology. In recent years, large-scale studies have shown that non-coding RNAs (ncRNAs) play an important role in the regulation of ovarian functions, which did not have the ability to encode proteins and could regulate hormone synthesis and germ cell development, differentiation, and apoptosis by silencing transposable elements and regulating coding genes. A number of researches by whole transcriptome sequencing of polycystic ovaries (PCO) from PCOS patients or PCOS model animals found that the abnormal expressions of many ncRNAs were involved in the regulation of ovarian dysfunctions of PCOS, including the development of oocytes, the microenvironment of follicular fluid, and the proliferation, differentiation, and apoptosis of granulosa cells. The present review focused on the roles of ncRNAs in the PCO of PCOS, in order to provide a theoretical basis for further understanding of the molecular mechanisms of PCO formation in PCOS.

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A Novel Method for LncRNA-Disease Association Prediction Based on an lncRNA-Disease Association Network

Cited by 89 publications

References 49 publications

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

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

An Iteration Model for Identifying Essential Proteins by Combining Comprehensive PPI Network With Biological Information

Roles of ncRNAs in Ovarian Dysfunction of Polycystic Ovary Syndrome

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