scholarly journals Prediction of circRNA-miRNA Associations Based on Network Embedding

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wei Lan ◽  
Mingrui Zhu ◽  
Qingfeng Chen ◽  
Jianwei Chen ◽  
Jin Ye ◽  
...  
Keyword(s):  
Heterogeneous Network ◽  
Noncoding Rna ◽  
Target Genes ◽  
Matrix Decomposition ◽  
Computational Method ◽  
Inner Product ◽  
Network Embedding ◽  
Mirna Sponge ◽  
Similarity Network ◽  
Fold Cross Validation

circRNA is a novel class of noncoding RNA with closed-loop structure. Increasing biological experiments have shown that circRNAs play an important role in many diseases by acting as a miRNA sponge to indirectly regulate the expression of miRNA target genes. Therefore, predicting associations between circRNAs and miRNAs can promote the understanding of pathogenesis of disease. In this paper, we propose a new computational method, NECMA, based on network embedding to predict potential associations between circRNAs and miRNAs. In our method, the Gaussian interaction profile (GIP) kernel similarities of circRNA and miRNA are calculated based on the known circRNA-miRNA associations, respectively. Then, the circRNA-miRNA association network, circRNA GIP kernel similarity network, and miRNA GIP kernel similarity network are utilized to construct the heterogeneous network. Furthermore, the network embedding algorithm is used to extract potential features of circRNA and miRNA from the heterogeneous network, respectively. Finally, the associations between circRNAs and miRNAs are predicted by using neighborhood regularization logic matrix decomposition and inner product. The performance of NECMA is evaluated by using ten-fold cross-validation. The results show that this method has better prediction accuracy than other state-of-the-art methods.

scholarly journals PWCDA: Path Weighted Method for Predicting circRNA-Disease Associations

2018 ◽  
Vol 19 (11) ◽  
pp. 3410 ◽  
Author(s):  
Xiujuan Lei ◽  
Zengqiang Fang ◽  
Luonan Chen ◽  
Fang-Xiang Wu
Keyword(s):  
Semantic Similarity ◽  
Heterogeneous Network ◽  
Functional Similarity ◽  
Computational Method ◽  
Related Gene ◽  
Similarity Network ◽  
Disease Pair ◽  
Disease Associations ◽  
Leave One Out ◽  
Similarity Scores

CircRNAs have particular biological structure and have proven to play important roles in diseases. It is time-consuming and costly to identify circRNA-disease associations by biological experiments. Therefore, it is appealing to develop computational methods for predicting circRNA-disease associations. In this study, we propose a new computational path weighted method for predicting circRNA-disease associations. Firstly, we calculate the functional similarity scores of diseases based on disease-related gene annotations and the semantic similarity scores of circRNAs based on circRNA-related gene ontology, respectively. To address missing similarity scores of diseases and circRNAs, we calculate the Gaussian Interaction Profile (GIP) kernel similarity scores for diseases and circRNAs, respectively, based on the circRNA-disease associations downloaded from circR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). Then, we integrate disease functional similarity scores and circRNA semantic similarity scores with their related GIP kernel similarity scores to construct a heterogeneous network made up of three sub-networks: disease similarity network, circRNA similarity network and circRNA-disease association network. Finally, we compute an association score for each circRNA-disease pair based on paths connecting them in the heterogeneous network to determine whether this circRNA-disease pair is associated. We adopt leave one out cross validation (LOOCV) and five-fold cross validations to evaluate the performance of our proposed method. In addition, three common diseases, Breast Cancer, Gastric Cancer and Colorectal Cancer, are used for case studies. Experimental results illustrate the reliability and usefulness of our computational method in terms of different validation measures, which indicates PWCDA can effectively predict potential circRNA-disease associations.

scholarly journals Predicting miRNA-Disease Association Based on Modularity Preserving Heterogeneous Network Embedding

2021 ◽  
Vol 9 ◽  
Author(s):  
Wei Peng ◽  
Jielin Du ◽  
Wei Dai ◽  
Wei Lan
Keyword(s):  
Heterogeneous Network ◽  
Structural Information ◽  
Disease Diagnosis ◽  
Disease Association ◽  
Biological Information ◽  
Vector Representation ◽  
Network Embedding ◽  
Prediction Ability ◽  
Similarity Network ◽  
Disease Associations

MicroRNAs (miRNAs) are a category of small non-coding RNAs that profoundly impact various biological processes related to human disease. Inferring the potential miRNA-disease associations benefits the study of human diseases, such as disease prevention, disease diagnosis, and drug development. In this work, we propose a novel heterogeneous network embedding-based method called MDN-NMTF (Module-based Dynamic Neighborhood Non-negative Matrix Tri-Factorization) for predicting miRNA-disease associations. MDN-NMTF constructs a heterogeneous network of disease similarity network, miRNA similarity network and a known miRNA-disease association network. After that, it learns the latent vector representation for miRNAs and diseases in the heterogeneous network. Finally, the association probability is computed by the product of the latent miRNA and disease vectors. MDN-NMTF not only successfully integrates diverse biological information of miRNAs and diseases to predict miRNA-disease associations, but also considers the module properties of miRNAs and diseases in the course of learning vector representation, which can maximally preserve the heterogeneous network structural information and the network properties. At the same time, we also extend MDN-NMTF to a new version (called MDN-NMTF2) by using modular information to improve the miRNA-disease association prediction ability. Our methods and the other four existing methods are applied to predict miRNA-disease associations in four databases. The prediction results show that our methods can improve the miRNA-disease association prediction to a high level compared with the four existing methods.

scholarly journals SNF-NN: computational method to predict drug-disease interactions using similarity network fusion and neural networks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tamer N. Jarada ◽  
Jon G. Rokne ◽  
Reda Alhajj
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Cross Validation ◽  
Drug Repositioning ◽  
Computational Method ◽  
Machine Learning Techniques ◽  
Similarity Network ◽  
Novel Drug ◽  
Similarity Information ◽  
Fold Cross Validation

Abstract Background Drug repositioning is an emerging approach in pharmaceutical research for identifying novel therapeutic potentials for approved drugs and discover therapies for untreated diseases. Due to its time and cost efficiency, drug repositioning plays an instrumental role in optimizing the drug development process compared to the traditional de novo drug discovery process. Advances in the genomics, together with the enormous growth of large-scale publicly available data and the availability of high-performance computing capabilities, have further motivated the development of computational drug repositioning approaches. More recently, the rise of machine learning techniques, together with the availability of powerful computers, has made the area of computational drug repositioning an area of intense activities. Results In this study, a novel framework SNF-NN based on deep learning is presented, where novel drug-disease interactions are predicted using drug-related similarity information, disease-related similarity information, and known drug-disease interactions. Heterogeneous similarity information related to drugs and disease is fed to the proposed framework in order to predict novel drug-disease interactions. SNF-NN uses similarity selection, similarity network fusion, and a highly tuned novel neural network model to predict new drug-disease interactions. The robustness of SNF-NN is evaluated by comparing its performance with nine baseline machine learning methods. The proposed framework outperforms all baseline methods ($$AUC-ROC$$ A U C - R O C = 0.867, and $$AUC-PR$$ A U C - P R =0.876) using stratified 10-fold cross-validation. To further demonstrate the reliability and robustness of SNF-NN, two datasets are used to fairly validate the proposed framework’s performance against seven recent state-of-the-art methods for drug-disease interaction prediction. SNF-NN achieves remarkable performance in stratified 10-fold cross-validation with $$AUC-ROC$$ A U C - R O C ranging from 0.879 to 0.931 and $$AUC-PR$$ A U C - P R from 0.856 to 0.903. Moreover, the efficiency of SNF-NN is verified by validating predicted unknown drug-disease interactions against clinical trials and published studies. Conclusion In conclusion, computational drug repositioning research can significantly benefit from integrating similarity measures in heterogeneous networks and deep learning models for predicting novel drug-disease interactions. The data and implementation of SNF-NN are available at http://pages.cpsc.ucalgary.ca/ tnjarada/snf-nn.php.

scholarly journals Predicting Long Noncoding RNA and Protein Interactions Using Heterogeneous Network Model

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ao Li ◽  
Mengqu Ge ◽  
Yao Zhang ◽  
Chen Peng ◽  
Minghui Wang
Keyword(s):  
Network Model ◽  
Protein Interaction ◽  
Protein Interactions ◽  
Heterogeneous Network ◽  
Noncoding Rna ◽  
Interaction Network ◽  
Computational Method ◽  
Protein Protein Interaction ◽  
Protein Interaction Prediction ◽  
Leave One Out

Recent study shows that long noncoding RNAs (lncRNAs) are participating in diverse biological processes and complex diseases. However, at present the functions of lncRNAs are still rarely known. In this study, we propose a network-based computational method, which is called lncRNA-protein interaction prediction based on Heterogeneous Network Model (LPIHN), to predict the potential lncRNA-protein interactions. First, we construct a heterogeneous network by integrating the lncRNA-lncRNA similarity network, lncRNA-protein interaction network, and protein-protein interaction (PPI) network. Then, a random walk with restart is implemented on the heterogeneous network to infer novel lncRNA-protein interactions. The leave-one-out cross validation test shows that our approach can achieve an AUC value of 96.0%. Some lncRNA-protein interactions predicted by our method have been confirmed in recent research or database, indicating the efficiency of LPIHN to predict novel lncRNA-protein interactions.

scholarly journals SNF-NN: Computational Method To Predict Drug-Disease Interactions Using Similarity Network Fusion and Neural Networks

2020 ◽  
Author(s):  
Tamer Jarada ◽  
Jon Rokne ◽  
Reda Alhajj
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Cross Validation ◽  
Drug Repositioning ◽  
Computational Method ◽  
Machine Learning Techniques ◽  
Similarity Network ◽  
Novel Drug ◽  
Similarity Information ◽  
Fold Cross Validation

Abstract Drug repositioning is an emerging approach in pharmaceutical research for identifying novel therapeutic potentials for approved drugs and discover therapies for untreated diseases. Due to its time and cost efficiency, drug repositioning plays an instrumental role in optimizing the drug development process compared to the traditional de novo drug discovery process. Advances in the genomics, together with the enormous growth of large-scale publicly available data and the availability of high-performance computing capabilities, have further motivated the development of computational drug repositioning approaches. More recently, the rise of machine learning techniques, together with the availability of powerful computers, has made the area of computational drug repositioning an area of intense activities. In this study, a novel framework SNF-NN based on deep learning is presented, where novel drugdisease interactions are predicted using drug-related similarity information, disease-related similarity information, and known drug-disease interactions. Heterogeneous similarity information related to drugs and disease is fed to the proposed framework in order to predict novel drug-disease interactions. SNF-NN uses similarity selection, similarity network fusion, and a highly tuned novel neural network model to predict new drug-disease interactions. The robustness of SNF-NN is evaluated by comparing its performance with nine baseline machine learning methods. The proposed framework outperforms all baseline methods (AUC − ROC = 0.867, and AUC − P R=0.876) using stratified 10-fold cross-validation. To further demonstrate the reliability and robustness of SNF-NN, two datasets are used to fairly validate the proposed framework’s performance against seven recent state-of-the-art methods for drug-disease interaction prediction. SNF-NN achieves remarkable performance in stratified 10-fold cross-validation with AUC − ROC ranging from 0.879 to 0.931 and AUC − P R from 0.856 to 0.903. Moreover, the efficiency of SNF-NN by is verified by validating predicted unknown drug-disease interactions against clinical trials and published studies. In conclusion, computational drug repositioning research can significantly benefit from integrating similarity measures in heterogeneous networks and deep learning models for predicting novel drug-disease interactions

A Survey of Network Embedding for Drug Analysis and Prediction

2020 ◽  
Vol 21 ◽  
Author(s):  
Zhixian Liu ◽  
Qingfeng Chen ◽  
Wei Lan ◽  
Jiahai Liang ◽  
Yiping Pheobe Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Protein Function ◽  
Dimensional Space ◽  
Auxiliary Information ◽  
Matrix Decomposition ◽  
Drug Analysis ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Network Embedding ◽  
Similarity Estimation

: Traditional network-based computational methods have shown good results in drug analysis and prediction. However, these methods are time consuming and lack universality, and it is difficult to exploit the auxiliary information of nodes and edges. Network embedding provides a promising way for alleviating the above problems by transforming network into a low-dimensional space while preserving network structure and auxiliary information. This thus facilitates the application of machine learning algorithms for subsequent processing. Network embedding has been introduced into drug analysis and prediction in the last few years, and has shown superior performance over traditional methods. However, there is no systematic review of this issue. This article offers a comprehensive survey of the primary network embedding methods and their applications in drug analysis and prediction. The network embedding technologies applied in homogeneous network and heterogeneous network are investigated and compared, including matrix decomposition, random walk, and deep learning. Especially, the Graph neural network (GNN) methods in deep learning are highlighted. Further, the applications of network embedding in drug similarity estimation, drug-target interaction prediction, adverse drug reactions prediction, protein function and therapeutic peptides prediction are discussed. Several future potential research directions are also discussed.

scholarly journals Sulforaphane Inhibits the Expression of Long Noncoding RNA H19 and Its Target APOBEC3G and Thereby Pancreatic Cancer Progression

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 827
Author(s):  
Yiqiao Luo ◽  
Bin Yan ◽  
Li Liu ◽  
Libo Yin ◽  
Huihui Ji ◽  
...  
Keyword(s):  
Cancer Progression ◽  
Colony Formation ◽  
Noncoding Rna ◽  
Target Genes ◽  
Gene Array ◽  
New Drugs ◽  
Tumor Promoter ◽  
Ductal Adenocarcinoma ◽  
Pilot Studies

Pancreatic ductal adenocarcinoma (PDAC) is extremely malignant and the therapeutic options available usually have little impact on survival. Great hope is placed on new therapeutic targets, including long noncoding RNAs (lncRNAs), and on the development of new drugs, based on e.g., broccoli-derived sulforaphane, which meanwhile has shown promise in pilot studies in patients. We examined whether sulforaphane interferes with lncRNA signaling and analyzed five PDAC and two nonmalignant cell lines, patient tissues (n = 30), and online patient data (n = 350). RT-qPCR, Western blotting, MTT, colony formation, transwell and wound healing assays; gene array analysis; bioinformatics; in situ hybridization; immunohistochemistry and xenotransplantation were used. Sulforaphane regulated the expression of all of five examined lncRNAs, but basal expression, biological function and inhibition of H19 were of highest significance. H19 siRNA prevented colony formation, migration, invasion and Smad2 phosphorylation. We identified 103 common sulforaphane- and H19-related target genes and focused to the virus-induced tumor promoter APOBEC3G. APOBEC3G siRNA mimicked the previously observed H19 and sulforaphane effects. In vivo, sulforaphane- or H19 or APOBEC3G siRNAs led to significantly smaller tumor xenografts with reduced expression of Ki67, APOBEC3G and phospho-Smad2. Together, we identified APOBEC3G as H19 target, and both are inhibited by sulforaphane in prevention of PDAC progression.

scholarly journals Pineal gland transcriptomic profiling reveals the differential regulation of lncRNA and mRNA related to prolificacy in STH sheep with two FecB genotypes

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Chunyan Li ◽  
Xiaoyun He ◽  
Zijun Zhang ◽  
Chunhuan Ren ◽  
Mingxing Chu
Keyword(s):  
Pineal Gland ◽  
Expression Pattern ◽  
Noncoding Rna ◽  
Target Genes ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Rna Seq ◽  
Important Regulator ◽  
Gsh Metabolism ◽  
Transcriptome Expression

Abstract Background Long noncoding RNA (lncRNA) has been identified as important regulator in hypothalamic-pituitary-ovarian axis associated with sheep prolificacy. However, little is known of their expression pattern and potential roles in the pineal gland of sheep. Herein, RNA-Seq was used to detect transcriptome expression pattern in pineal gland between follicular phase (FP) and luteal phase (LP) in FecBBB (MM) and FecB++ (ww) STH sheep, respectively, and differentially expressed (DE) lncRNAs and mRNAs associated with reproduction were identified. Results Overall, 135 DE lncRNAs and 1360 DE mRNAs in pineal gland between MM and ww sheep were screened. Wherein, 39 DE lncRNAs and 764 DE mRNAs were identified (FP vs LP) in MM sheep, 96 DE lncRNAs and 596 DE mRNAs were identified (FP vs LP) in ww sheep. Moreover, GO and KEGG enrichment analysis indicated that the targets of DE lncRNAs and DE mRNAs were annotated to multiple biological processes such as phototransduction, circadian rhythm, melanogenesis, GSH metabolism and steroid biosynthesis, which directly or indirectly participate in hormone activities to affect sheep reproductive performance. Additionally, co-expression of lncRNAs-mRNAs and the network construction were performed based on correlation analysis, DE lncRNAs can modulate target genes involved in related pathways to affect sheep fecundity. Specifically, XLOC_466330, XLOC_532771, XLOC_028449 targeting RRM2B and GSTK1, XLOC_391199 targeting STMN1, XLOC_503926 targeting RAG2, XLOC_187711 targeting DLG4 were included. Conclusion All of these differential lncRNAs and mRNAs expression profiles in pineal gland provide a novel resource for elucidating regulatory mechanism underlying STH sheep prolificacy.

scholarly journals MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Léo Pio-Lopez ◽  
Alberto Valdeolivas ◽  
Laurent Tichit ◽  
Élisabeth Remy ◽  
Anaïs Baudot
Keyword(s):  
Heterogeneous Networks ◽  
Heterogeneous Network ◽  
Link Prediction ◽  
Network Embedding ◽  
Multiplex Networks ◽  
Multiplex Network ◽  
Gene Associations ◽  
Different Types ◽  
Embedding Methods ◽  
Node Embeddings

AbstractNetwork embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their effectiveness in tasks such as community detection, node classification, and link prediction. However, very few network embedding methods have been specifically designed to handle multiplex networks, i.e. networks composed of different layers sharing the same set of nodes but having different types of edges. Moreover, to our knowledge, existing approaches cannot embed multiple nodes from multiplex-heterogeneous networks, i.e. networks composed of several multiplex networks containing both different types of nodes and edges. In this study, we propose MultiVERSE, an extension of the VERSE framework using Random Walks with Restart on Multiplex (RWR-M) and Multiplex-Heterogeneous (RWR-MH) networks. MultiVERSE is a fast and scalable method to learn node embeddings from multiplex and multiplex-heterogeneous networks. We evaluate MultiVERSE on several biological and social networks and demonstrate its performance. MultiVERSE indeed outperforms most of the other methods in the tasks of link prediction and network reconstruction for multiplex network embedding, and is also efficient in link prediction for multiplex-heterogeneous network embedding. Finally, we apply MultiVERSE to study rare disease-gene associations using link prediction and clustering. MultiVERSE is freely available on github at https://github.com/Lpiol/MultiVERSE.

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