scholarly journals SPMLMI: predicting lncRNA–miRNA interactions in humans using a structural perturbation method

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11426
Author(s):  
Mingmin Xu ◽  
Yuanyuan Chen ◽  
Wei Lu ◽  
Lingpeng Kong ◽  
Jingya Fang ◽  
...  
Keyword(s):  
Perturbation Method ◽  
Model Performance ◽  
Similarity Network ◽  
Structural Perturbation ◽  
Non Coding Rna ◽  
Structural Consistency ◽  
Long Non Coding Rna ◽  
Important Basis ◽  
Lncrna Similarity Network ◽  
Better Than

Long non-coding RNA (lncRNA)–microRNA (miRNA) interactions are quickly emerging as important mechanisms underlying the functions of non-coding RNAs. Accordingly, predicting lncRNA–miRNA interactions provides an important basis for understanding the mechanisms of action of ncRNAs. However, the accuracy of the established prediction methods is still limited. In this study, we used structural consistency to measure the predictability of interactive links based on a bilayer network by integrating information for known lncRNA–miRNA interactions, an lncRNA similarity network, and an miRNA similarity network. In particular, by using the structural perturbation method, we proposed a framework called SPMLMI to predict potential lncRNA–miRNA interactions based on the bilayer network. We found that the structural consistency of the bilayer network was higher than that of any single network, supporting the utility of bilayer network construction for the prediction of lncRNA–miRNA interactions. Applying SPMLMI to three real datasets, we obtained areas under the curves of 0.9512 ± 0.0034, 0.8767 ± 0.0033, and 0.8653 ± 0.0021 based on 5-fold cross-validation, suggesting good model performance. In addition, the generalizability of SPMLMI was better than that of the previously established methods. Case studies of two lncRNAs (i.e., SNHG14 and MALAT1) further demonstrated the feasibility and effectiveness of the method. Therefore, SPMLMI is a feasible approach to identify novel lncRNA–miRNA interactions underlying complex biological processes.

scholarly journals Class similarity network for coding and long non-coding RNA classification

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yu Zhang ◽  
Yahui Long ◽  
Chee Keong Kwoh
Keyword(s):  
Neural Network ◽  
Validation Dataset ◽  
Functional Study ◽  
Similarity Network ◽  
Non Coding Rna ◽  
Comparison Results ◽  
General Similarity ◽  
Non Coding Rnas ◽  
High Level ◽  
Long Non Coding Rna

Abstract Background Long non-coding RNAs (lncRNAs) play significant roles in varieties of physiological and pathological processes.The premise of the lncRNA functional study is that the lncRNAs are identified correctly. Recently, deep learning method like convolutional neural network (CNN) has been successfully applied to identify the lncRNAs. However, the traditional CNN considers little relationships among samples via an indirect way. Results Inspired by the Siamese Neural Network (SNN), here we propose a novel network named Class Similarity Network in coding RNA and lncRNA classification. Class Similarity Network considers more relationships among input samples in a direct way. It focuses on exploring the potential relationships between input samples and samples from both the same class and the different classes. To achieve this, Class Similarity Network trains the parameters specific to each class to obtain the high-level features and represents the general similarity to each class in a node. The comparison results on the validation dataset under the same conditions illustrate the superiority of our Class Similarity Network to the baseline CNN. Besides, our method performs effectively and achieves state-of-the-art performances on two test datasets. Conclusions We construct Class Similarity Network in coding RNA and lncRNA classification, which is shown to work effectively on two different datasets by achieving accuracy, precision, and F1-score as 98.43%, 0.9247, 0.9374, and 97.54%, 0.9990, 0.9860, respectively.

scholarly journals Link prediction of viral spike proteins and cell receptors using structural perturbation method

2021 ◽  
Author(s):  
Iris Zhou
Keyword(s):  
Perturbation Method ◽  
Amino Acid Sequences ◽  
New Host ◽  
Major Barrier ◽  
Similarity Network ◽  
Cell Receptors ◽  
Structural Perturbation ◽  
Protein Receptors ◽  
Human Viruses ◽  
Spike Proteins

Abstract Many protein receptors for animal and human viruses have been discovered in decades of studies. The main determinant of virus entry is the binding of the viral spike protein to host cell receptors, which mediates membrane fusion. In this work, a bilayer network is constructed by integrating the similarity network of the viral spike proteins, the similarity network of host receptors, and the association network between viruses and receptors. The structural perturbation method (SPM) is used to predict possible emerging infection of a virus in potential new host organisms. The reliability of this method is based on the hypothesis that the major barrier to virus infection is the differences in the compatibility of spike proteins and cell receptors, which is determined by the amino acid sequences among species.

scholarly journals SPRDA: a matrix completion approach based on the structural perturbation to infer disease-associated Piwi-Interacting RNAs

2020 ◽  
Author(s):  
Kai Zheng ◽  
Zhu-Hong You ◽  
Lei Wang ◽  
Leon Wong ◽  
Zhao-hui Zhan
Keyword(s):  
Perturbation Method ◽  
High Performance ◽  
Large Scale ◽  
Matrix Completion ◽  
Disease Diagnosis ◽  
Diagnosis And Treatment ◽  
Biological Research ◽  
Sequence Information ◽  
Similarity Network ◽  
Structural Perturbation

AbstractEmerging evidence suggests that PIWI-interacting RNAs (piRNAs) are one of the most influential small non-coding RNAs (ncRNAs) that regulate RNA silencing. piRNA and PIWI proteins have been confirmed for disease diagnosis and treatment as novel biomarkers due to its abnormal expression in various cancers. However, the current research is not strong enough to further clarify the functions of piRNA in cancer and its underlying mechanism. Therefore, how to provide large-scale and serious piRNA candidates for biological research has grown up to be a pressing issue. The main motivation of this work is tantamount to fill the gap in research on large-scale prediction of disease-related piRNAs. In this study, a novel computational model based on the structural perturbation method is proposed, called SPRDA. In detail, the duplex network is constructed based on the piRNA similarity network and disease similarity network extracted from piRNA sequence information, Gaussian interaction profile kernel similarity information and gene-disease association information. The structural perturbation method is then used to predict the potential associations on the duplex network, which is more predictive than other network structures in terms of structural consistency. In the five-fold cross-validation, SPRDA shows high performance on the benchmark dataset piRDisease, with an AUC of 0.9529. Furthermore, the predictive performance of SPRDA for 10 diseases shows the robustness of the proposed method. Overall, the proposed approach can provide unique insights into the pathogenesis of the disease and will advance the field of oncology diagnosis and treatment.

scholarly journals Link prediction of viral spike proteins and cell receptors using structural perturbation method

2021 ◽  
Author(s):  
Iris Zhou
Keyword(s):  
Perturbation Method ◽  
Amino Acid Sequences ◽  
New Host ◽  
Major Barrier ◽  
Similarity Network ◽  
Cell Receptors ◽  
Structural Perturbation ◽  
Protein Receptors ◽  
Human Viruses ◽  
Spike Proteins

Abstract Many protein receptors for animal and human viruses have been discovered in decades of studies. The main determinant of virus entry is the binding of the viral spike protein to host cell receptors, which mediates membrane fusion.In this work, a bilayer network is constructed by integrating the similarity network of the viral spike proteins, the similarity network of host receptors, and the association network between viruses and receptors. The structural perturbation method (SPM) is used to predict possible emerging infection of a virus in potential new host organisms. The reliability of this method is based on the hypothesis that the major barrier to virus infection is the differences in the compatibility of spike proteins and cell receptors, which is determined by the amino acid sequences among species.

Diagnostic efficacy of long non-coding RNA in lung cancer: a systematic review and meta-analysis

2018 ◽  
Vol 94 (1116) ◽  
pp. 578-587 ◽  
Author(s):  
Shui-Ping Dai ◽  
Jing Jin ◽  
Wei-Min Li
Keyword(s):  
Lung Cancer ◽  
Meta Analysis ◽  
Area Under The Curve ◽  
Small Cell Lung ◽  
Diagnostic Efficacy ◽  
Lung Cancer Diagnosis ◽  
Non Coding Rna ◽  
Novel Method ◽  
Long Non Coding Rna ◽  
Better Than

The detection of long non-coding RNA (lncRNA) is a novel method for lung cancer diagnosis. However, the diagnostic efficacy of lncRNA in different studies is inconsistent. Therefore, we conducted this meta-analysis to elucidate the diagnostic efficacy of lncRNA in identification of lung cancer including small cell lung cancer. The online PubMed, Medline, EMBASE, CNKI and Wanfang literature databases were searched to identify all related articles about the diagnostic efficacy of lncRNA for lung cancer. 28 articles including 3044 patients with lung cancer and 2598 controls were enrolled in our meta-analysis. lncRNA sustained a high diagnostic efficacy, pooled sensitivity of 0.82 (95% CI 0.79 to 0.84), specificity of 0.82 (95% CI 0.78 to 0.84) and area under the curve (AUC) of 0.88 (95% CI 0.85 to 0.91) in identification of patients with lung cancer from controls. Furthermore, the diagnostic efficacy of paralleled lncRNA was better than single lncRNA (sensitivity: 0.86 vs 0.80; specificity: 0.88 vs 0.78; AUC: 0.93 vs 0.86). MALAT1 had a better diagnostic efficacy than GAS5 (AUC: 0.90 vs 0.81; sensitivity: 0.83 vs 0.70; specificity: 0.83 vs 0.78). lncRNA in tissues was observed to achieve lower diagnostic efficacy than that in plasma or serum (AUC: 0.87 vs 0.90 vs 0.90) when stratified by sample types. In summary, our meta-analysis suggests that lncRNA might be a promising biomarker(s) for identifying lung cancer and the combination of lncRNA or with other biomarkers had a better diagnostic efficacy.

scholarly journals Prediction of potential disease-associated microRNAs using structural perturbation method

2017 ◽  
Author(s):  
Xiangxiang Zeng ◽  
Li Liu ◽  
Linyuan Lü ◽  
Quan Zou
Keyword(s):  
Perturbation Method ◽  
Experimental Studies ◽  
Computational Method ◽  
Identification Accuracy ◽  
Supplementary Information ◽  
Disease Network ◽  
Similarity Network ◽  
Structural Perturbation ◽  
Disease Associations ◽  
Reliable Performance

AbstractMotivationThe identification of disease-related microRNAs(miRNAs) is an essential but challenging task in bioinformatics research. Similarity-based link prediction methods are often used to predict potential associations between miRNAs and diseases. In these methods, all unobserved associations are ranked by their similarity scores. Higher score indicates higher probability of existence. However, most previous studies mainly focus on designing advanced methods to improve the prediction accuracy while neglect to investigate the link predictability of the networks that present the miRNAs and diseases associations. In this work, we construct a bilayer network by integrating the miRNA–disease network, the miRNA similarity network and the disease similarity network. We use structural consistency as an indicator to estimate the link predictability of the related networks. On the basis of the indicator, a derivative algorithm, called structural perturbation method (SPM), is applied to predict potential associations between miRNAs and diseases.ResultsThe link predictability of bilayer network is higher than that of miRNA–disease network, indicating that the prediction of potential miRNAs-diseases associations on bilayer network can achieve higher accuracy than based merely on the miRNA–disease network. A comparison between the SPM and other algorithms reveals the reliable performance of SPM which performed well in a 5-fold cross-validation. We test fifteen networks. The AUC values of SPM are higher than some well-known methods, indicating that SPM could serve as a useful computational method for improving the identification accuracy of miRNA-disease associations. Moreover, in a case study on breast neoplasm, 80% of the top-20 predicted miRNAs have been manually confirmed by previous experimental studies.Availability and Implementationhttps://github.com/lecea/[email protected], [email protected] informationSupplementary data are available at Bioinformatics online.

Innate immune responses and type 1 diabetes: A role for a long non-coding RNA?

2014 ◽  
Vol 9 (S 01) ◽  
Author(s):  
MP Ashton ◽  
I Tan ◽  
L Mackin ◽  
C Elso ◽  
E Chu ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Immune Responses ◽  
Innate Immune ◽  
Innate Immune Responses ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Long non-coding RNA expression profiles in human parathyroid tumors

2017 ◽  
Author(s):  
Annamaria Morotti ◽  
Irene Forno ◽  
Valentina Andre ◽  
Andrea Terrasi ◽  
Chiara Verdelli ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Rna Expression ◽  
Parathyroid Tumors ◽  
Non Coding Rna ◽  
Long Non Coding Rna

The Variant rs145204276 of GAS5 is Associated with the Development and Prognosis of Gastric Cancer

2018 ◽  
Vol 27 (1) ◽  
pp. 19-24 ◽  
Author(s):  
Qianjun Li ◽  
Gang Ma ◽  
Huimin Guo ◽  
Suhua Sun ◽  
Ying Xu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Survival Rate ◽  
Cancer Progression ◽  
Regulatory Mechanism ◽  
Tumor Stage ◽  
Kaplan Meier ◽  
Non Coding Rna ◽  
Early Tumor ◽  
Long Non Coding Rna ◽  
Clinicopathological Variables

Background & Aims: Down-regulation of the growth arrest specific transcript 5 (GAS5) (long non-coding RNA) is associated with cell proliferation of gastric cancer (GC) and a poor prognosis. We aimed to investigate whether the variant rs145204276 of GAS5 is associated with the prognosis of GC in the Chinese population, and to unveil the regulatory mechanism underlying the GAS5 expression in GC tissues.Method: 1,253 GC patients and 1,354 healthy controls were included. The frequency of the genotype del/del and the allele del of rs145204276 were compared between the patients and the controls and between different subgroups of patients classified by clinicopathological variables. The overall survival rate was analyzed according to the Kaplan-Meier method using the log-rank test.Results: The frequency of genotype del/del was significantly lower in patients than in the controls (7.0% vs. 9.1%, p = 0.001). Kaplan-Meier analysis showed that genotype del/del was significantly associated with a higher survival rate (p = 0.01). Patients with late tumor stage were found to have a significantly lower rate of genotype del/del than those with an early tumor stage (4.9% vs. 8.8%, p = 0.01). Patients with UICC III and IV were found to have a significantly lower rate of genotype del/del than those with UICC I and II (5.3% vs. 8.1%, p = 0.02).Conclusion: The variant rs145204276 of GAS5 is associated with the development and prognosis of GC. The allele del of rs145204276 is associated with a remarkably lower incidence of cancer progression and metastasis.

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