Dự đoán liên kết trong mạng hỗn tạp và ứng dụng trong dự đoán mối quan hệ giữa RNA không mã hóa và bệnh

MINISTRY OF EDUCATION AND TRAINING HANOI NATIONAL UNIVERSITY OF EDUCATION NGUYEN VAN TINH LINK PREDICTION IN HETEROGENEOUS INFORMATION NETWORKS AND ITS APPLICATIONS IN PREDICTING ASSOCIATIONS BETWEEN NON-CODING RNAS AND DISEASES DOCTORAL DISSERTATION IN COMPUTER SCIENCE HANOI-2023 MINISTRY OF EDUCATION AND TRAINING HANOI NATIONAL UNIVERSITY OF EDUCATION NGUYEN VAN TINH LINK PREDICTION IN HETEROGENEOUS INFORMATION NETWORKS AND ITS APPLICATIONS IN PREDICTING ASSOCIATIONS BETWEEN NON-CODING RNAS AND DISEASES Major: Computer Science Code: 9480101 DOCTORAL DISSERTATION IN COMPUTER SCIENCE SUPERVISORS Assoc Prof Dr TRAN DANG HUNG Dr LE THI TU KIEN Hanoi-2023 i AUTHORSHIP'S DECLARATION I, NGUYEN VAN TINH, affirm that the dissertation entitled “Link prediction in heterogeneous information networks and its applications in predicting associations between non-coding RNAs and diseases” has been completed by myself under the supervision of Assoc.Prof.Dr Tran Dang Hung and Dr Le Thi Tu Kien I assure some points as follows: - This dissertation was done in the Ph.D research time at Hanoi National University of Education - This work has not been submitted for any other degrees or qualifications at Hanoi National University of Education or any other institutions - Appropriate acknowledgment has been given in the thesis where references have been made to the other published works - The submitted thesis is my own, except the work in the collaboration has been included The collaborative contributions have been indicated Hanoi, 2023 Ph.D Student SUPERVISORS: Assoc Prof Dr TRAN DANG HUNG Dr LE THI TU KIEN ii ACKNOWLEDGEMENT The dissertation was completed in duration of my Ph.D course at Hanoi National University of Education (HNUE) HNUE is a special place where I obtained valuable knowledge and skills on the way to become a researcher I am so grateful for all the people who always support and encourage me completing the dissertation Firstly, I would to say thanks to my advisors, Assoc Prof Dr Tran Dang Hung and Dr Le Thi Tu Kien for their instruction, advice, and encouragement throughout my Ph.D course My dissertation could not be completed without my advisors’ scientific direction, encouragement, and support Secondly, I wish to thank all members of the Faculty of Information Technology, HNUE for their frequent support during my Ph.D course And I also wish to thank all my colleagues in the Faculty of Information Technology, Hanoi University of Industry (HaUI) for their support in professional work during the time of the Ph.D course Next, I wish to thank Assoc Prof Dr Than Quang Khoat, Hanoi University of Science and Technology, and Dr Nguyen Tran Quoc Vinh, Faculty of Information Technology, The University of Da Nang - University of Science and Education for their contributions and suggestions during my Ph.D course And then, I also would like to thank all reviewers for their valuable comments and suggestions on the dissertation’s completion Additionally, this work was funded by Gia Lam Urban Development and Investment Company Limited, Vingroup and Supported by Vingroup Innovation Foundation (VINIF) under project code VINIF.2019 DA18 Finally, I would like to express my sincere gratitude to my family and friends for their continuous support and encouragement to complete the Ph.D course Hanoi, 2023 Ph.D Student Nguyen Van Tinh iii CONTENTS AUTHORSHIP'S DECLARATION i ACKNOWLEDGEMENT ii CONTENTS iii ABBREVIATIONS vi LIST OF TABLES vii LIST OF FIGURES viii INTRODUCTION CHAPTER BACKGROUND 10 1.1 Basic concepts 10 1.1.1 Heterogeneous information networks 11 1.1.2 Biological systems 13 1.1.3 Non-coding RNAs (ncRNAs) 14 1.2 Link prediction in heterogeneous information networks 15 1.2.1 Link prediction problem 15 1.2.2 Link prediction methods 16 1.2.3 Link prediction applications in biological systems 19 1.3 Computational methods for predicting associations between non-coding RNAs and diseases 22 1.3.1 Predicting non coding RNA-disease association prediction as a link prediction problem 22 1.3.2 Materials used for ncRNA-disease association prediction 22 1.3.3 Similarity calculation and network construction 26 1.3.4 Literature review of computational methods to predict ncRNA-disease associations 27 1.4 Thesis’s research directions 36 1.5 Some evaluation methods and metrics to evaluate prediction performance 37 1.5.1 Cross-validation 37 1.5.2 Area under Roc Curve (AUC) 38 iv 1.5.3 Area under Precision-Recall Curve (AUPR) 39 1.5.4 Checking case studies 40 1.6 Chapter summary 41 CHAPTER NCRNA-DISEASE ASSOCIATIONS PREDICTION WITH COLLABORATIVE FILTERING AND RESOURCE ALLOCATION PROCESS ON A TRIPARTITE GRAPH 43 2.1 Motivations 43 2.2 Main related works 45 2.2.1 The item-based collaborative filtering algorithm for ncRNA-disease association prediction 45 2.2.2 Resource allocation on a tripartite graph 46 2.3 The proposed model for predicting ncRNA-disease associations based on a collaborative filtering algorithm and a resource allocation process on a tripartite graph 48 2.4 Employing the proposed model to infer miRNA-disease associations based on collaborative filtering and resource allocation 50 2.4.1 Detailed description of proposed model's stages in inferring miRNAdisease associations 50 2.4.2 Proposed method's experiments and results 54 2.5 Employing the proposed model to predict lncRNA-disease associations based on collaborative filtering and resource allocation 66 2.5.1 Detailed description of proposed model's stages in predicting lncRNAdisease associations 66 2.5.2 Proposed method’s experiments and results 71 2.6 Chapter summary 79 CHAPTER MIRNA-DISEASE ASSOCIATIONS PREDICTION USING IMPROVED RANDOM WALK WITH RESTART AND INTEGRATING MULTIPLE SIMILARITIES 81 3.1 Motivation and main related works 81 v 3.2 Datasets used in the proposed method 83 3.2.1 Human miRNA-disease associations 83 3.2.2 Disease semantic similarity 83 3.2.3 MiRNA functional similarity 84 3.3 Proposed method 85 3.3.1 Proposed method overview 85 3.3.2 Calculating Gaussian interaction profile kernel similarity for miRNAs and diseases 87 3.3.3 Calculating Integrated similarity for miRNAs and diseases 88 3.3.4 Weighted K-nearest known neighbors algorithm 88 3.3.5 Constructing miRNA similarity-based and disease similarity based heterogeneous networks 89 3.3.6 Employing improved random walk with restart to predict miRNA-disease associations 91 3.3.7 Rank the final prediction score of associations to obtain predicted miRNA-disease associations 94 3.4 Experiments and results 94 3.4.1 Datasets 94 3.4.2 Implementing and Estimating time complexity of the proposed method 95 3.4.3 Performance measures 96 3.4.4 Performance comparison with other related models 100 3.4.4 Case studies 102 3.5 Chapter summary and discussion 108 CONCLUSION AND FUTURE WORKS 110 PUBLICATIONS 113 REFERENCES 114 vi ABBREVIATIONS No 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Abbreviation AUC AUPR CF CNN CRC DAGs DBN FN FP FPR GCN GIP HCC HF HIN lncRNAs LOOCV MF miRNAs ncRNAs NMF OAG POAG ROC RWR SVM TN TP TPR WKNKN Meaning Area Under Roc Curve Area Under Precision-Recall Curve Collaborative filtering Convolutional neural network Colorectal cancer Directed acrylic graphs Deep brief network False negative False positive False positive rate Graph convolutional network Gaussian interaction profile Hepatocellular carcinoma Heart failure Heterogeneous information network Long non-coding RNAs Leave-one-out cross validation Matrix factorization Micro RNAs Non-coding RNAs Non-negative matrix factorization Open-angle glaucoma Primary open-angle glaucoma Receiver operating characteristic Random Walk with Restart Support vector machine True negative True positive True positive rate Weighted K nearest known neighbors vii LIST OF TABLES Table 1.1 Databases containing miRNA-related information and miRNA-disease associations 23 Table 1.2 Databases containing lncRNA-related information 24 Table 2.1 Performance comparison with other related models 60 Table 2.2 Top 40 predicted miRNAs for Prostatic Neoplasms 62 Table 2.3 Top 40 predicted miRNAs for Heart failure 63 Table 2.4 Top 40 predicted miRNAs for Glioma 64 Table 2.5 Top 20 miRNAs for Glaucoma, Open-Angle 65 Table 2.6 AUC and AUPR values of related methods in comparison 76 Table 2.7 Top 10 predicted Prostate cancer-related lncRNAs 78 Table 2.8 Top 10 predicted lncRNAs related to Stomach cancer 78 Table 3.1 AUC and AUPR One-sample t-test 97 Table 3.2 Evaluation of index changes in WKNKN algorithm 99 Table 3.3 AUC and AUPR values RWRMMDA and other latest methods in comparison 102 Table 3.4 Top 40 predicted Breast Neoplasms-associated miRNAs 103 Table 3.5 Top 40 predicted Hepatocellular carcinoma-associated miRNAs 105 Table 3.6 Top 40 predicted Stomach Neoplasms-associated miRNAs 106 Table 3.7 Top 10 predicted associations between Lung Neoplasms and miRNAs from the simulated experiment for predicting new disease-related miRNAs 107 Table 3.8 Top 10 predicted associations for Ovarian Neoplasms and miRNAs from the simulated experiment for predicting new disease-related miRNAs 108 viii LIST OF FIGURES Figure 0.1 The dissertation outline Figure 1.1 An illustration of HIN with multiple node types and multiple link types 11 Figure 1.2 An illustration of HIN’s network schema 12 Figure 1.3 An illustration of a link prediction problem 16 Figure 1.4 A ROC curve and AUC's illustration 39 Figure 1.5 An illustration of a Precision-recall curve and AUPR 40 Figure 2.1 The proposed model's flowchart 49 Figure 2.2 The datasets and the numbers of data nodes in the proposed method 56 Figure 2.3 ROC curve and AUC value of the proposed method with γ = 0.9 in one experimental running time 59 Figure 2.4 Precision-Recall curve and AUPR value of the proposed method with γ = 0.9 in one experimental running time 60 Figure 2.5 The relationships between the different data sources and the numbers of data nodes used in the proposed method 72 Figure 2.6 The proposed method's ROC curves and AUC values in running times of experiments with 𝛾 = 0.8 75 Figure 2.7 The proposed method's Precision-Recall curves and AUPR values in running times of experiments with 𝛾 = 0.8 76 Figure 3.1 Illustration of computing miRNA functional similarity 84 Figure 3.2 The workflow of the proposed method (RWRMMDA) 85 Figure 3.3 Illustration of the process of weight assignment in disease space and miRNA space 91 Figure 3.4 The improved RWR process's steps to predict miRNA-disease associations 92 Figure 3.5 ROC curves and AUC values (a) and PR curves and AUPR values (b) in running times of 5-fold cross-validation experiments 97 119 http://arxiv.org/abs/1910.04807 49 Dadu A et al (2018), "A Study of Link Prediction Using Deep Learning", in ICAICR 2018, CCIS 955, 2018, pp 377–385 doi: 10.1007/978-981-13-31404 50 Wang X et al (2021), "Link prediction in heterogeneous information networks: An improved deep graph convolution approach", Decis Support Syst., vol 141, no September 2020, p 113448, doi: 10.1016/j.dss.2020.113448 51 Shang D et al (2014), "Prioritizing candidate disease metabolites based on global functional relationships between metabolites in the context of metabolic pathways", PLoS One, vol 9, no 8, pp 1–11, doi: 10.1371/journal.pone.0104934 52 Pinu F R et al (2019), "Systems biology and multi-omics integration: Viewpoints from the metabolomics research community", Metabolites, vol 9, no 4, pp 1–31, doi: 10.3390/metabo9040076 53 Hao M., Bryant S H., and Wang Y (2017), "Predicting drug-target interactions by dual-network integrated logistic matrix factorization", Sci Rep., vol 7, no June 2016, pp 1–11, doi: 10.1038/srep40376 54 Berenstein A J et al (2016), "A Multilayer Network Approach for Guiding Drug Repositioning in Neglected Diseases", PLoS Negl Trop Dis., vol 10, no 1, pp 1–33, doi: 10.1371/journal.pntd.0004300 55 Qu J et al (2018), "Inferring potential small molecule–miRNA association based on triple layer heterogeneous network", J Cheminform., vol 10, no 1, pp 1–14, doi: 10.1186/s13321-018-0284-9 56 Luo H et al (2018), "Computational drug repositioning using low-rank matrix approximation and randomized algorithms", Bioinformatics, vol 34, no 11, pp 1904–1912, doi: 10.1093/bioinformatics/bty013 57 Martínez V et al (2015), "DrugNet: Network-based drug-disease prioritization by integrating heterogeneous data", Artif Intell Med., vol 63, no 1, pp 41– 120 49, doi: 10.1016/j.artmed.2014.11.003 58 Xuan Z et al (2019), "A probabilistic matrix factorization method for identifying lncRNA-disease associations", Genes (Basel)., vol 10, no 2, doi: 10.3390/genes10020126 59 Xu J et al (2019), "Identifying Potential miRNAs–Disease Associations With Probability Matrix Factorization", Front Genet., vol 10, no December, p 1234, doi: 10.3389/fgene.2019.01234 60 Jiang Q et al (2010), "Prioritization of disease microRNAs through a human phenome-microRNAome network", BMC Syst Biol., vol 4, no SUPPL 1, p S2, doi: 10.1186/1752-0509-4-S1-S2 61 Gu C et al (2016), "Network Consistency Projection for Human miRNADisease Associations Inference", Sci Rep., vol 6, no October, p 36054, doi: 10.1038/srep36054 62 Chen X et al (2018), "BNPMDA: Bipartite network projection for MiRNA– Disease association prediction", Bioinformatics, vol 34, no 18, pp 3178– 3186, doi: 10.1093/bioinformatics/bty333 63 Liang C., Yu S., and Luo J (2019), "Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs", PLoS Comput Biol., vol 15, no 4, p e1006931, doi: 10.1371/journal.pcbi.1006931 64 Yu G et al (2017), "BRWLDA: Bi-random walks for predicting lncRNAdisease associations", Oncotarget, vol 8, no 36, pp 60429–60446, doi: 10.18632/oncotarget.19588 65 Gu C et al (2017), "Global network random walk for predicting potential human lncRNA-disease associations", Sci Rep., vol 7, no 1, pp 1–11, doi: 10.1038/s41598-017-12763-z 66 Xiao X et al (2018), "BPLLDA: Predicting lncRNA-disease associations based on simple paths with limited lengths in a heterogeneous network", Front Genet., vol 9, no OCT, p 411, doi: 10.3389/fgene.2018.00411 67 Chen X., Liu M X., and Yan G Y (2012), "RWRMDA: Predicting novel 121 human microRNA-disease associations", Mol Biosyst., vol 8, no 10, pp 2792–2798, doi: 10.1039/c2mb25180a 68 Xuan P et al (2015), "Prediction of potential disease-associated microRNAs based on random walk", Bioinformatics, vol 31, no 11, pp 1805–1815, doi: 10.1093/bioinformatics/btv039 69 Sun D et al (2016), "NTSMDA: prediction of miRNA–disease associations by integrating network topological similarity", Mol Biosyst., vol 12, no 7, pp 2224–2232, doi: 10.1039/C6MB00049E 70 Le D et al (2017), "Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs", BMC Bioinformatics, vol 18, p 479, doi: 10.1186/s12859-017-1924-1 71 Niu Y W et al (2019), "Integrating random walk and binary regression to identify novel miRNA-disease association", BMC Bioinformatics, vol 20, no 1, p 59, doi: 10.1186/s12859-019-2640-9 72 Li A et al (2021), "A novel miRNA-disease association prediction model using dual random walk with restart and space projection federated method", PLoS One, vol 16, no June 2021, p e0252971, doi: 10.1371/journal.pone.0252971 73 Fan X et al (2019), "Prediction of lncRNA-disease associations by integrating diverse heterogeneous information sources with RWR algorithm and positive pointwise mutual information", BMC Bioinformatics, pp 1–12 74 Wang L E I (2019), "IIRWR : Internal Inclined Random Walk With Restart for LncRNA-Disease Association Prediction", IEEE Access, vol 7, pp 54034– 54041, doi: 10.1109/ACCESS.2019.2912945 75 Liu Y et al (2019), "A Novel Network-Based Computational Model for Prediction of Potential LncRNA – Disease Association", doi: 10.3390/ijms20071549 76 Li G et al (2019), "Prediction of LncRNA-Disease Associations Based on Network Consistency Projection", IEEE Access, vol 7, pp 58849–58856, doi: 122 10.1109/ACCESS.2019.2914533 77 Li Z S., Liu B., and Yan C (2019), "CFMDA: collaborative filtering-based MiRNA-disease association prediction", Multimed Tools Appl., vol 78, no 1, pp 605–618, doi: 10.1007/s11042-017-5291-8 78 Liu J X et al (2021), "DSCMF: prediction of LncRNA-disease associations based on dual sparse collaborative matrix factorization", BMC Bioinformatics, vol 22, pp 1–18, doi: 10.1186/s12859-020-03868-w 79 Fu G et al (2018), "Matrix factorization-based data fusion for the prediction of lncRNA-disease associations", Bioinformatics, vol 34, no 9, pp 1529– 1537, doi: 10.1093/bioinformatics/btx794 80 Zeng M et al (2020), "SDLDA: lncRNA-disease association prediction based on singular value decomposition and deep learning", Methods, vol 179, no May, pp 73–80, doi: 10.1016/j.ymeth.2020.05.002 81 Shen Z et al (2017), "MiRNA-disease association prediction with collaborative matrix factorization", Complexity, vol 2017, doi: 10.1155/2017/2498957 82 Lan W et al (2017), "LDAP : a web server for lncRNA-disease association prediction", vol 33, pp 458–460, doi: 10.1093/bioinformatics/btw639 83 Chen X., Wu Q F., and Yan G Y (2017), "RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction", RNA Biol., vol 14, no 7, pp 952–962, doi: 10.1080/15476286.2017.1312226 84 Chen X., Zhu C C., and Yin J (2019), "Ensemble of decision tree reveals potential miRNA-disease associations", PLoS Comput Biol., vol 15, no 7, p e1007209, doi: 10.1371/journal.pcbi.1007209 85 Chen X and Yan G Y (2013), "Novel human lncRNA-disease association inference based on lncRNA expression profiles", Bioinformatics, vol 29, no 20, pp 2617–2624, doi: 10.1093/bioinformatics/btt426 86 Chen X and Yan G Y (2014), "Semi-supervised learning for potential human microRNA-disease associations inference", Sci Rep., vol 4, p 5501, doi: 123 10.1038/srep05501 87 Chen X and Huang L (2017), "LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction", PLoS Comput Biol., vol 13, no 12, p e1005912, doi: 10.1371/journal.pcbi.1005912 88 Wang B (2018), "Multiple Linear Regression Analysis of lncRNA – Disease", vol 2018 89 Yao D et al (2020), "A random forest based computational model for predicting novel lncRNA-disease associations", BMC Bioinformatics, vol 21, no 1, pp 1–18, doi: 10.1186/s12859-020-3458-1 90 Fraidouni N and Zaruba G (2019), "A Matrix Completion Approach for Predicting lncRNA-disease association", Int’l Conf Bioinforma Comput Biol., pp 1–6 91 Li W et al (2019), "Inferring Latent Disease-lncRNA Associations by Faster Matrix Completion on a Heterogeneous Network", Front Genet., vol 10, no September, pp 1–15, doi: 10.3389/fgene.2019.00769 92 Chen X et al (2018), "Predicting miRNA-disease association based on inductive matrix completion", Bioinformatics, vol 34, no 24, pp 4256–4265, doi: 10.1093/bioinformatics/bty503 93 Yu S P et al (2019), "MCLPMDA: A novel method for miRNA-disease association prediction based on matrix completion and label propagation", J Cell Mol Med., vol 23, no 2, pp 1427–1438, doi: 10.1111/jcmm.14048 94 Xuan P et al (2019), "CNNDLP: A method based on convolutional autoencoder and convolutional neural network with adjacent edge attention for predicting lncrna–disease associations", Int J Mol Sci., vol 20, no 17, doi: 10.3390/ijms20174260 95 Xuan P et al (2019), "Dual Convolutional Neural Networks With Attention Mechanisms Based Method for Predicting Disease-Related lncRNA Genes", Front Genet., vol 10, no May, pp 1–11, doi: 10.3389/fgene.2019.00416 124 96 Peng J et al (2019), "A learning-based framework for miRNA-disease association identification using neural networks", Bioinformatics, vol 35, no 21, pp 4364–4371, doi: 10.1093/bioinformatics/btz254 97 Tian Q., Zhou S., and Wu Q (2022), "A miRNA-Disease Association Identification Method Based on Reliable Negative Sample Selection and Improved Single-Hidden Layer Feedforward Neural Network", Inf., vol 13, no 3, doi: 10.3390/info13030108 98 Madhavan M and Gopakumar G (2020), "DBNLDA: Deep Belief Network based representation learning for lncRNA-disease association prediction", arXiv Prepr arXiv, no 2006:12534., doi: 10.1007/s10489-021-02675-x 99 Ding Y et al (2020), "Deep belief network–Based Matrix Factorization Model for MicroRNA-Disease Associations Prediction", Evol Bioinforma., vol 16, doi: 10.1177/1176934320919707 100 Chen X et al (2021), "Deep-belief network for predicting potential miRNAdisease associations", Brief Bioinform., vol 22, no 3, pp 1–10, doi: 10.1093/bib/bbaa186 101 Li J et al (2020), "Neural inductive matrix completion with graph convolutional Bioinformatics, networks vol for 36, miRNA-disease no 8, association pp prediction", 2538–2546, doi: 10.1093/bioinformatics/btz965 102 Fu H et al (2022), "MVGCN: data integration through multi-view graph convolutional network for predicting links in biomedical bipartite networks", Bioinformatics, vol 38, no 2, pp 426–434, doi: 10.1093/bioinformatics/btab651 103 Shi Z et al (2021), "A representation learning model based on variational inference and graph autoencoder for predicting lncRNA-disease associations", BMC Bioinformatics, vol 22, no 1, pp 1–20, doi: 10.1186/s12859-021-04073z 104 Zeng M et al (2020), "DMFLDA: A deep learning framework for predicting 125 IncRNA–disease associations", IEEE/ACM Trans Comput Biol Bioinforma., p 1, doi: 10.1109/tcbb.2020.2983958 105 Ji C et al (2021), "AEMDA: inferring miRNA-disease associations based on deep autoencoder", Bioinformatics, vol 37, no 1, pp 66–72, doi: 10.1093/bioinformatics/btaa670 106 Mørk S et al (2014), "Protein-driven inference of miRNA-disease associations", Bioinformatics, vol 30, no 3, pp 392–397, doi: 10.1093/bioinformatics/btt677 107 Shi H et al (2013), "Walking the interactome to identify human miRNAdisease associations through the functional link between miRNA targets and disease genes", BMC Syst Biol., vol 7, doi: 10.1186/1752-0509-7-101 108 Guo Z H et al (2019), "A Learning-Based Method for LncRNA-Disease Association Identification Combing Similarity Information and Rotation Forest", iScience, vol 19, pp 786–795, doi: 10.1016/j.isci.2019.08.030 109 Guo Z H et al (2019), Combining High Speed ELM with a CNN Feature Encoding to Predict LncRNA-Disease Associations, vol 11644 LNCS Springer International Publishing, 2019 doi: 10.1007/978-3-030-26969-2_39 110 Berrar D (2019), "Cross-validation", Encycl Bioinforma Comput Biol Acad Press., vol 1, pp 542–545, doi: 10.1016/B978-0-12-809633-8.20349-X 111 K H.-T (2013), "Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation", Casp J Intern Med 2013;, vol 4(2), pp 627–635 112 Saito T and Rehmsmeier M (2015), "The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets", PLoS One, vol 10, no 3, p e0118432., doi: 10.1371/journal.pone.0118432 113 Sarwar B., Karypis G., and Konstan J (2001), "Item-based collaborative filtering recommendation algorithms", WWW ’01 Proc 10th Int Conf World Wide Web, pp 285–295 126 114 Alaimo S., Giugno R., and Pulvirenti A (2014), "ncPred: ncRNA-disease association prediction through tripartite network-based inference", Front Bioeng Biotechnol., vol 2, no DEC, doi: 10.3389/fbioe.2014.00071 115 Zhang Z K., Zhou T., and Zhang Y C (2010), "Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs", Phys A Stat Mech its Appl., vol 389, no 1, pp 179–186, doi: 10.1016/j.physa.2009.08.036 116 Liu N N., He L., and Zhao M (2013), "Social temporal collaborative ranking for context aware movie recommendation", ACM Trans Intell Syst Technol., vol 4, no 1, doi: 10.1145/2414425.2414440 117 Zhao H et al (2018), "Prediction of microRNA-disease associations based on distance correlation set", BMC Bioinformatics, vol 19, no 1, pp 1–14, doi: 10.1186/s12859-018-2146-x 118 Li J et al (2014), "starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein – RNA interaction networks from large-scale CLIP-Seq data", vol 42, no December 2013, pp 92–97, doi: 10.1093/nar/gkt1248 119 Cui T et al (2018), "MNDR v2.0: An updated resource of ncRNA-disease associations in mammals", Nucleic Acids Res., vol 46, no D1, pp D371– D374, doi: 10.1093/nar/gkx1025 120 Bao Z et al (2019), "LncRNADisease 2.0: An updated database of long noncoding RNA-associated diseases", Nucleic Acids Res., vol 47, no D1, pp D1034–D1037, doi: 10.1093/nar/gky905 121 Li Y et al (2014), "HMDD v2 0 : a database for experimentally supported human microRNA and disease associations", Nucleic Acids Res., vol 42, no Database, pp 1070–1074, doi: 10.1093/nar/gkt1023 122 McGuire S (2016), "World Cancer Report 2014 Geneva, Switzerland: World Health Organization, International Agency for Research on Cancer, WHO Press, 2015", Adv Nutr., vol 7, no 2, pp 418–419, doi: 10.3945/an.116.012211 123 Fu Q et al (2019), "SOX30, a target gene of miR-653-5p, represses the 127 proliferation and invasion of prostate cancer cells through inhibition of Wnt/βcatenin signaling", Cell Mol Biol Lett., vol 24, no 1, pp 1–13, doi: 10.1186/s11658-019-0195-4 124 Budd W T et al (2015), "Dual action of miR-125b as a tumor suppressor and OncomiR-22 promotes prostate cancer tumorigenesis", PLoS One, vol 10, no 11, pp 1–21, doi: 10.1371/journal.pone.0142373 125 Chen Y et al (2017), "The association of heart failure-related microRNAs with neurohormonal signaling ☆", BBA - Mol Basis Dis., vol 1863, no 8, pp 2031– 2040, doi: 10.1016/j.bbadis.2016.12.019 126 Wei X J et al (2015), "Biological significance of miR-126 expression in atrial fibrillation and heart failure", Brazilian J Med Biol Res., vol 48, pp 983– 989 127 Bernardo B C et al (2012), "Therapeutic inhibition of the miR-34 family attenuates pathological cardiac remodeling and improves heart function", Proc Natl Acad Sci U S A., vol 109, no 43, pp 17615–17620, doi: 10.1073/pnas.1206432109 128 van Middendorp L B et al (2017), "Local microRNA-133a downregulation is associated with hypertrophy in the dyssynchronous heart", ESC Hear Fail., vol 4, no 3, pp 241–251, doi: 10.1002/ehf2.12154 129 Zhou Q et al (2018), "MicroRNAs as potential biomarkers for the diagnosis of glioma: A systematic review and meta-analysis", Cancer Sci., vol 109, no 9, pp 2651–2659, doi: 10.1111/cas.13714 130 Vaitkiene P et al (2019), "Association of miR-34a expression with quality of life of glioblastoma patients: A prospective study", Cancers (Basel)., vol 11, no 3, pp 1–11, doi: 10.3390/cancers11030300 131 Yuan M et al (2018), "MicroRNA (miR) 125b regulates cell growth and invasion in pediatric low grade glioma", Sci Rep., vol 8, no 1, pp 1–14, doi: 10.1038/s41598-018-30942-4 132 Luo G et al (2017), "MicroRNA-21 promotes migration and invasion of 128 glioma cells via activation of Sox2 and β-catenin signaling", Mol Med Rep., vol 15, no 1, pp 187–193, doi: 10.3892/mmr.2016.5971 133 Shaikh Y., Yu F., and Coleman A L (2014), "Burden of undetected and untreated glaucoma in the United States", Am J Ophthalmol., vol 158, no 6, pp 1121-1129.e1, doi: 10.1016/j.ajo.2014.08.023 134 Drewry M D et al (2018), "Differentially expressed microRNAs in the aqueous humor of patients with exfoliation glaucoma or primary open-angle glaucoma", Hum Mol Genet., vol 27, no 7, pp 1263–1275, doi: 10.1093/hmg/ddy040 135 Hindle A G et al (2019), "Identification of candidate miRNA biomarkers for glaucoma", Investig Ophthalmol Vis Sci., vol 60, no 1, pp 134–146, doi: 10.1167/iovs.18-24878 136 Qin W et al (2016), "Down-regulation of miR-34a promotes the cell proliferation and inhibits apoptosis in glaucoma", Int J Clin Exp Pathol, vol 9, no 2, pp 1368–1375 137 Ning S et al (2016), "Lnc2Cancer : a manually curated database of experimentally supported lncRNAs associated with various human cancers", vol 44, no October 2015, pp 980–985, doi: 10.1093/nar/gkv1094 138 Chen G et al (2013), "LncRNADisease : a database for long-non-coding RNA-associated diseases", vol 41, no November 2012, pp 983–986, doi: 10.1093/nar/gks1099 139 Lu Z., Cohen K B., and Hunter L (2009), "GeneRIF QUALITY ASSURANCE AS SUMMARY REVISION", no 1999, pp 269–280 140 Malik R et al (2014), "The lncRNA PCAT29 inhibits oncogenic phenotypes in prostate cancer", Mol Cancer Res., vol 12, no 8, pp 1081–1087, doi: 10.1158/1541-7786.MCR-14-0257 141 Yu Y et al (2020), "lncRNA UCA1 Functions as a ceRNA to Promote Prostate Cancer Progression via Sponging miR143", Mol Ther - Nucleic Acids, vol 19, no March, pp 751–758, doi: 10.1016/j.omtn.2019.11.021 129 142 Rawla P and Barsouk A (2019), "Epidemiology of gastric cancer: Global trends, risk factors and prevention", Prz Gastroenterol., vol 14, no 1, pp 26– 38, doi: 10.5114/pg.2018.80001 143 Yuan L et al (2020), "Long non-coding RNAs towards precision medicine in gastric cancer: Early diagnosis, treatment, and drug resistance", Mol Cancer, vol 19, no 1, pp 1–22, doi: 10.1186/s12943-020-01219-0 144 Xu W et al (2020), "Circulating lncRNA SNHG11 as a novel biomarker for early diagnosis and prognosis of colorectal cancer", Int J Cancer, vol 146, no 10, pp 2901–2912, doi: 10.1002/ijc.32747 145 Luo J and Xiao Q (2017), "A novel approach for predicting microRNAdisease associations by unbalanced bi-random walk on heterogeneous network", J Biomed Inform., vol 66, pp 194–203, doi: 10.1016/j.jbi.2017.01.008 146 Luo J and Long Y (2020), "NTSHMDA: Prediction of Human MicrobeDisease Association Based on Random Walk by Integrating Network Topological Similarity", IEEE/ACM Trans Comput Biol Bioinforma., vol 17, no 4, pp 1341–1351, doi: 10.1109/TCBB.2018.2883041 147 Wang D et al (2010), "Inferring the human microRNA functional similarity and functional Bioinformatics, network vol based 26, on no microRNA-associated 13, pp diseases", 1644–1650, doi: 10.1093/bioinformatics/btq241 148 Chen X et al (2016), "WBSMDA: Within and between Score for MiRNADisease Association prediction", Sci Rep., vol 6, no November 2015, p 21106, doi: 10.1038/srep21106 149 Lu M et al (2008), "An Analysis of Human MicroRNA and Disease Associations", PLoS One, vol 3, no 10, p e3420, doi: 10.1371/journal.pone.0003420 150 Li G et al (2022), "Predicting miRNA-disease associations based on graph attention network with multi-source information", BMC Bioinformatics, vol 130 23, no 1, pp 1–24, doi: 10.1186/s12859-022-04796-7 151 Huang D et al (2022), "Computational method using heterogeneous graph convolutional network model combined with reinforcement layer for MiRNA– disease association prediction", BMC Bioinformatics, vol 23, no 1, pp 1–19, doi: 10.1186/s12859-022-04843-3 152 Xie X et al (2023), "Predicting miRNA ‑ disease associations based on PPMI and attention network", BMC Bioinformatics, pp 1–19, doi: 10.1186/s12859023-05152-z 153 Singh R and Mo Y (2013), "Role of microRNAs in breast cancer", Cancer Biol Ther., vol 14, no 3, pp 201–212 154 Zografos E et al (2019), "Prognostic role of microRNAs in breast cancer: A systematic review", Oncotarget, vol 10, no 67, pp 7156–7178, doi: 10.18632/oncotarget.27327 155 Imani S., Wu R C., and Fu J (2018), "MicroRNA-34 family in breast cancer: From research to therapeutic potential", J Cancer, vol 9, no 20, pp 3765– 3775, doi: 10.7150/jca.25576 156 Li L et al (2013), "MiR-34a inhibits proliferation and migration of breast cancer through down-regulation of Bcl-2 and SIRT1", Clin Exp Med., vol 13, no 2, pp 109–117, doi: 10.1007/s10238-012-0186-5 157 Xu X et al (2018), "The role of MicroRNAs in hepatocellular carcinoma", J Cancer, vol 9, no 19, pp 3557–3569, doi: 10.7150/jca.26350 158 O’Connor S et al (2010), "Hepatocellular Carcinoma — United States, 2001– 2006", Morb Mortal Wkly Rep., vol 59, no 17, pp 517–520 159 Balogh J et al (2016), "Hepatocellular carcinoma: a review", J Hepatocell Carcinoma, vol Volume 3, pp 41–53, [Online] Available: https://www.dovepress.com/hepatocellular-carcinoma-a-review-peerreviewed-article-JHC 160 Zhang Z et al (2015), "MicroRNA-146a inhibits cancer metastasis by downregulating VEGF through dual pathways in hepatocellular carcinoma", 131 Mol Cancer, vol 14, no 1, p 5, doi: 10.1186/1476-4598-14-5 161 Zhou Y et al (2018), "Hepatocellular carcinoma-derived exosomal miRNA21 contributes to tumor progression by converting hepatocyte stellate cells to cancer-associated fibroblasts", J Exp Clin Cancer Res., vol 37, no 1, p 324, doi: 10.1186/s13046-018-0965-2 162 Rong M.-H et al (2019), "Overexpression of MiR-452-5p in hepatocellular carcinoma tissues and its prospective signaling pathways.", Int J Clin Exp Pathol., vol 12, no 11, pp 4041–4056, [Online] Available: http://www.ncbi.nlm.nih.gov/pubmed/31933800%0Ahttp://www.pubmedcent ral.nih.gov/articlerender.fcgi?artid=PMC6949781 163 Xia Q et al (2019), "Identification of novel biomarkers for hepatocellular carcinoma using transcriptome analysis", J Cell Physiol., vol 234, no 4, pp 4851–4863, doi: 10.1002/jcp.27283 164 Zhang H., Chen X., and Yuan Y (2020), "Investigation of the miRNA and mRNA Coexpression Network and Their Prognostic Value in Hepatocellular Carcinoma", Biomed Res Int., vol 2020, p Article ID 8726567, doi: 10.1155/2020/8726567 165 Yu L et al (2015), "miR-454 functions as an oncogene by inhibiting CHD5 in hepatocellular carcinoma", Oncotarget, vol 6, no 36, pp 39225–39234, doi: 10.18632/oncotarget.4407 166 Wu G et al (2016), "MicroRNA-655-3p functions as a tumor suppressor by regulating ADAM10 and β-catenin pathway in Hepatocellular Carcinoma", J Exp Clin Cancer Res., vol 35, no 1, p 89, doi: 10.1186/s13046-016-0368-1 167 Zhang C et al (2018), "Downregulation of microRNA-376a in gastric cancer and association with poor prognosis", Cell Physiol Biochem., vol 51, no 5, pp 2010–2018, doi: 10.1159/000495820 168 Gong J et al (2014), "Characterization of microRNA-29 family expression and investigation of their mechanistic roles in gastric cancer", Carcinogenesis, vol 35, no 2, pp 497–506, doi: 10.1093/carcin/bgt337 132 169 Feng Y et al (2018), "Dysregulated microrna expression profiles in gastric cancer cells with high peritoneal metastatic potential", Exp Ther Med., vol 16, no 6, pp 4602–4608, doi: 10.3892/etm.2018.6783 170 Lu Q et al (2019), "MicroRNA-181a functions as an oncogene in gastric cancer by targeting caprin-1", Front Pharmacol., vol 9, p 1565, doi: 10.3389/fphar.2018.01565 171 Li H et al (2019), "MicroRNA-183 affects the development of gastric cancer by regulating autophagy via MALAT1-miR-183-SIRT1 axis and PI3K/AKT/mTOR signals", Artif Cells, Nanomedicine Biotechnol., vol 47, no 1, pp 3163–3171, doi: 10.1080/21691401.2019.1642903 172 Zhenkai Wang et al (2018), "The Role of mir-152 and DNMT1 in Gastric Cancer Cell Proliferation and Invasion", Gastroenterol Hepatol Res., vol 3, no 1, p 011, doi: 10.24966/ghr-2566/100011 173 Peng Y et al (2014), "MicroRNA-338 inhibits growth, invasion and metastasis of gastric cancer by targeting NRP1 expression", PLoS One, vol 9, no 4, p e94422, doi: 10.1371/journal.pone.0094422 174 Wu K L et al (2019), "The roles of microRNA in lung cancer", Int J Mol Sci., vol 20, p 1611, doi: 10.3390/ijms20071611 175 Liao J et al (2020), "MicroRNA-based biomarkers for diagnosis of non-small cell lung cancer (NSCLC)", Thorac Cancer, vol 11, pp 762–768, doi: 10.1111/1759-7714.13337 176 Staicu C E et al (2020), "Role of microRNAs as Clinical Cancer Biomarkers for Ovarian Cancer: A Short Overview", Cells, vol 9, no 1, p 169, doi: 10.3390/cells9010169 177 Zhang S et al (2018), "Identification of common differentially-expressed mirnas in ovarian cancer cells and their exosomes compared with normal ovarian surface epithelial cell cells", Oncol Lett., vol 16, no 2, pp 2391–2401, doi: 10.3892/ol.2018.8954 178 Alshamrani A A (2020), "Roles of microRNAs in Ovarian Cancer 133 Tumorigenesis: Two Decades Later, What Have We Learned?", Front Oncol., vol 10, no July, p 1084, doi: 10.3389/fonc.2020.01084