A comprehensive survey on computational methods of non-coding RNA and disease association prediction

Mattick, 2006, Non-coding RNA, Hum Mol Genet, 15, R17, 10.1093/hmg/ddl046

Taft, 2010, Non-coding RNAs: regulators of disease, J Pathol, 220, 126, 10.1002/path.2638

Eddy, 2001, Non-coding RNA genes and the modern RNA world, Nat Rev Genet, 2, 919, 10.1038/35103511

Dhahbi, 2014, Circulating small noncoding RNAs as biomarkers of aging, Ageing Res Rev, 17, 86, 10.1016/j.arr.2014.02.005

Esmaeili, 2020, Role of non-coding RNAs as novel biomarkers for detection of colorectal cancer progression through interaction with the cell signaling pathways, Gene, 753, 10.1016/j.gene.2020.144796

Ghafouri-Fard, 2020, A comprehensive review of non-coding RNAs functions in multiple sclerosis, Eur J Pharmacol, 879, 10.1016/j.ejphar.2020.173127

Esteller, 2011, Non-coding RNAs in human disease, Nat Rev Genet, 12, 861, 10.1038/nrg3074

Ghafouri-Fard, 2019, Nuclear enriched abundant transcript 1 (NEAT1): a long non-coding RNA with diverse functions in tumorigenesis, Biomed Pharmacother, 111, 51, 10.1016/j.biopha.2018.12.070

Kazimierczyk, 2020, Human long noncoding rna interactome: detection, characterization and function, Int J Mol Sci, 21, 1027, 10.3390/ijms21031027

Doxtater, 2020, Recent advances on the role of long non-coding RNAs in Alzheimer's disease, Neural Regen Research, 15, 2253, 10.4103/1673-5374.284990

Solomon, 2020, MicroRNA's - the vibrant performers in the oral cancer scenario, Jpn Dent Sci Rev, 56, 85, 10.1016/j.jdsr.2020.04.001

Luo, 2017, A novel approach for predicting microRNA-disease associations by unbalanced bi-random walk on heterogeneous network, J Biomed Inform, 66, 194, 10.1016/j.jbi.2017.01.008

Chen, 2018, Ensemble learning and link prediction for miRNA-disease association prediction, RNA Biol, 15, 807

Chen, 2018, Predicting microRNA-disease associations using bipartite local models and hubness-aware regression, RNA Biol, 15, 1192, 10.1080/15476286.2018.1517010

Qu, 2019, Prediction of potential miRNA-disease associations using matrix decomposition and label propagation, Knowl Based Syst, 186, 10.1016/j.knosys.2019.104963

Wang, 2019, An integrated framework for the identification of potential miRNA-disease association based on novel negative samples extraction strategy, RNA Biol, 16, 257, 10.1080/15476286.2019.1568820

Chen, 2013, Novel human lncRNA-disease association inference based on lncRNA expression profiles, Bioinformatics, 29, 2617, 10.1093/bioinformatics/btt426

Sun, 2014, Inferring novel lncRNA-disease associations based on a random walk model of a lncRNA functional similarity network, Mol Biosyst, 10, 2074, 10.1039/C3MB70608G

Lan, 2016, LDAP: a web server for lncRNA-disease association prediction, Bioinformatics, 33, 458, 10.1093/bioinformatics/btw639

Lu, 2018, Prediction of lncRNA-disease associations based on inductive matrix completion, Bioinformatics, 34, 3357, 10.1093/bioinformatics/bty327

Zhang, 2019, Integrating multiple heterogeneous networks for novel lncRNA-disease association inference, IEEE/ACM Trans Comput Biol Bioinform, 16, 396, 10.1109/TCBB.2017.2701379

Fan, 2018, Prediction of circrna-disease associations using KATZ model based on heterogeneous networks, Int J Biol Sci, 14, 1950, 10.7150/ijbs.28260

Lei, 2018, PWCDA: path weighted method for predicting circRNA-disease associations, Int J Mol Sci, 19, 3410, 10.3390/ijms19113410

Zeng, 2019, Predicting disease-associated circular RNAs using deep forests combined with positive-unlabeled learning methods, Brief Bioinform, 21, 1425, 10.1093/bib/bbz080

Wang, 2019, Predicting circRNA-disease associations based on circrna expression similarity and functional similarity, Front Genet, 10, 832, 10.3389/fgene.2019.00832

Wang, 2020, GCNCDA: a new method for predicting circRNA-disease associations based on graph convolutional network algorithm, PLoS Comput Biol, 16, e1007568, 10.1371/journal.pcbi.1007568

Chen, 2019, MicroRNAs and complex diseases: from experimental results to computational models, Brief Bioinform, 20, 515, 10.1093/bib/bbx130

Chen, 2017, Long non-coding RNAs and complex diseases: from experimental results to computational models, Brief Bioinform, 18, 558

Luo, 2020, Biomedical data and computational models for drug repositioning: a comprehensive review, Brief Bioinform, 10, bbz176

Bartel, 2004, MicroRNAs: genomics, biogenesis, mechanism, and function, Cell, 116, 281

Huang, 2019, HMDD v3.0: a database for experimentally supported human microRNA-disease associations, Nucleic Acids Res, 47, D1013, 10.1093/nar/gky1010

Jiang, 2009, miR2Disease: a manually curated database for microRNA deregulation in human disease, Nucleic Acids Res, 37, D98, 10.1093/nar/gkn714

Yang, 2017, dbDEMC 2.0: updated database of differentially expressed miRNAs in human cancers, Nucleic Acids Res, 45, D812, 10.1093/nar/gkw1079

Xie, 2013, miRCancer: a microRNA-cancer association database constructed by text mining on literature, Bioinformatics, 29, 638, 10.1093/bioinformatics/btt014

Barupal, 2015, ExcellmiRDB for translational genomics: a curated online resource for extracellular microRNAs, Omics, 19, 24, 10.1089/omi.2014.0106

Ruepp, 2010, PhenomiR: a knowledgebase for microRNA expression in diseases and biological processes, Genome Biol, 11, R6, 10.1186/gb-2010-11-1-r6

Bao, 2019, LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases, Nucleic Acids Res, 47, D1034, 10.1093/nar/gky905

Gao, 2019, Lnc2Cancer v2.0: updated database of experimentally supported long non-coding RNAs in human cancers, Nucleic Acids Res, 47, D1028, 10.1093/nar/gky1096

Cui, 2018, MNDR v2.0: an updated resource of ncRNA-disease associations in mammals, Nucleic Acids Res, 46, D371

Fan, 2018, CircR2Disease: a manually curated database for experimentally supported circular RNAs associated with various diseases, Database (Oxford), 2018, 10.1093/database/bay044

Zhao, 2018, circRNA disease: a manually curated database of experimentally supported circRNA-disease associations, Cell Death Dis, 9, 2, 10.1038/s41419-018-0503-3

Yao, 2018, Circ2Disease: a manually curated database of experimentally validated circRNAs in human disease, Sci Rep, 8, 10.1038/s41598-018-29360-3

Rophina, 2020, Circad: a comprehensive manually curated resource of circular RNA associated with diseases, Database, 2020, 10.1093/database/baaa019

Griffiths-Jones, 2006, miRBase: microRNA sequences, targets and gene nomenclature, Nucleic Acids Res, 34, D140, 10.1093/nar/gkj112

Panwar, 2017, miRmine: a database of human miRNA expression profiles, Bioinformatics, 33, 1554, 10.1093/bioinformatics/btx019

Sarver, 2010, S-MED: sarcoma microRNA expression database, Lab Invest, 90, 753, 10.1038/labinvest.2010.53

Dweep, 2011, miRWalk--database: prediction of possible miRNA binding sites by "walking" the genes of three genomes, J Biomed Inform, 44, 839, 10.1016/j.jbi.2011.05.002

Taccioli, 2009, UCbase & miRfunc: a database of ultraconserved sequences and microRNA function, Nucleic Acids Res, 37, D41, 10.1093/nar/gkn702

Yang, 2011, miREnvironment database: providing a bridge for microRNAs, environmental factors and phenotypes, Bioinformatics, 27, 3329, 10.1093/bioinformatics/btr556

Bhattacharya, 2016, SomamiR 2.0: a database of cancer somatic mutations altering microRNA-ceRNA interactions, Nucleic Acids Res, 44, D1005, 10.1093/nar/gkv1220

Zhao, 2020, LncTarD: a manually-curated database of experimentally-supported functional lncRNA-target regulations in human diseases, Nucleic Acids Res, 48, D118

Zhou, 2017, ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data, Nucleic Acids Res, 45, D43, 10.1093/nar/gkw965

Zheng, 2015, deepBase v2.0: identification, expression, evolution and function of small RNAs, LncRNAs and circular RNAs from deep-sequencing data, Nucleic Acids Res, 44, D196, 10.1093/nar/gkv1273

Cheng, 2018, DincRNA: a comprehensive web-based bioinformatics toolkit for exploring disease associations and ncRNA function, Bioinformatics, 34, 1953, 10.1093/bioinformatics/bty002

Cheng, 2019, LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse, Nucleic Acids Res, 47, D140, 10.1093/nar/gky1051

Quek, 2015, lncRNAdb v2.0: expanding the reference database for functional long noncoding RNAs, Nucleic Acids Res, 43, D168, 10.1093/nar/gku988

Glažar, 2014, circBase: a database for circular RNAs, RNA, 20, 1666, 10.1261/rna.043687.113

Li, 2018, exoRBase: a database of circRNA, lncRNA and mRNA in human blood exosomes, Nucleic Acids Res, 46, D106, 10.1093/nar/gkx891

Ghosal, 2013, Circ2Traits: a comprehensive database for circular RNA potentially associated with disease and traits, Front Genet, 4, 283, 10.3389/fgene.2013.00283

Li, 2014, starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data, Nucleic Acids Res, 42, D92, 10.1093/nar/gkt1248

Xia, 2018, CSCD: a database for cancer-specific circular RNAs, Nucleic Acids Res, 46, D925, 10.1093/nar/gkx863

Dudekula, 2016, CircInteractome: a web tool for exploring circular RNAs and their interacting proteins and microRNAs, RNA Biol, 13, 34, 10.1080/15476286.2015.1128065

Liu, 2019, Circbank: a comprehensive database for circRNA with standard nomenclature, RNA Biol, 16, 899, 10.1080/15476286.2019.1600395

Meng, 2019, CircFunBase: a database for functional circular RNAs, Database (Oxford), 10.1093/database/baz003

Schriml, 2019, Human disease ontology 2018 update: classification, content and workflow expansion, Nucleic Acids Res, 47, D955, 10.1093/nar/gky1032

Amberger, 2015, OMIM.org: online Mendelian inheritance in man (OMIM®), an online catalog of human genes and genetic disorders, Nucleic Acids Res, 43, D789, 10.1093/nar/gku1205

Piñero, 2017, DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants, Nucleic Acids Res, 45, D833, 10.1093/nar/gkw943

Wang, 2010, Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases, Bioinformatics, 26, 1644, 10.1093/bioinformatics/btq241

Zhou, 2014, Human symptoms-disease network, Nat Commun, 5, 10.1038/ncomms5212

Laarhoven, 2011, Gaussian interaction profile kernels for predicting drug–target interaction, Bioinformatics, 27, 3036, 10.1093/bioinformatics/btr500

Ni, 2020, Constructing disease similarity networks based on disease module theory, IEEE/ACM Trans Comput Biol Bioinform, 17, 906, 10.1109/TCBB.2018.2817624

Li, 2019, FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs, BMC Syst Biol, 13, 10.1186/s12918-019-0696-9

Luo, 2016, Collective prediction of disease-associated miRNAs based on transduction learning, IEEE/ACM Trans Comput Biol Bioinform, 14, 1468, 10.1109/TCBB.2016.2599866

Huang, 2018, Constructing prediction models from expression profiles for large scale lncRNA-miRNA interaction profiling, Bioinformatics, 34, 812, 10.1093/bioinformatics/btx672

Cock, 2009, Biopython: freely available python tools for computational molecular biology and bioinformatics, Bioinformatics, 25, 1422, 10.1093/bioinformatics/btp163

Ge, 2019, Predicting human disease-associated circRNAs based on locality-constrained linear coding, Genomics, 112, 1335, 10.1016/j.ygeno.2019.08.001

Lei, 2019, Predicting circRNA-disease associations based on improved collaboration filtering recommendation system with multiple data, Front Genet, 10, 897, 10.3389/fgene.2019.00897

Zhang, 2019, Predicting circRNA-disease associations through linear neighborhood label propagation method, IEEE Access, 7, 83474, 10.1109/ACCESS.2019.2920942

Hu, 2018, Identifying diseases-related metabolites using random walk, BMC Bioinformatics, 19, 10.1186/s12859-018-2098-1

Fogaras, 2005, Towards scaling fully personalized PageRank: algorithms, lower bounds, and experiments, Internet Mathematics, 2, 333, 10.1080/15427951.2005.10129104

Katz, 1953, A new status index derived from sociometric analysis, Psychoetrika, 18, 39, 10.1007/BF02289026

Zou, 2015, Prediction of microRNA-disease associations based on social network analysis methods, Biomed Res Int, 2015, 10.1155/2015/810514

Shi, 2013, Hetesim: a general framework for relevance measure in heterogeneous networks, IEEE Trans Knowl Data Eng, 26, 2479, 10.1109/TKDE.2013.2297920

Herlocker, 1999, International Acm Sigir Conference on Research & Development in Information Retrieval

Zhou, 2010, Solving the apparent diversity-accuracy dilemma of recommender systems, Proc Natl Acad Sci U S A, 107, 4511, 10.1073/pnas.1000488107

Lee, 1999, Learning the parts of objects by non-negative matrix factorization, Nature, 401, 788, 10.1038/44565

Zhang, 2020, A graph regularized generalized matrix factorization model for predicting links in biomedical bipartite networks, Bioinformatics, 36, 3474, 10.1093/bioinformatics/btaa157

Billsus, 1998, Learning collaborative information filters. In: Proceedings of the Fifteenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc, 46

Zeng, 2020, SDLDA: lncRNA-disease association prediction based on singular value decomposition and deep learning, Methods, 179, 73, 10.1016/j.ymeth.2020.05.002

Cai, 2011, Graph regularized nonnegative matrix factorization for data representation, IEEE Trans Pattern Anal Mach Intell, 33, 1548, 10.1109/TPAMI.2010.231

Xiao, 2018, A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations, Bioinformatics, 34, 239, 10.1093/bioinformatics/btx545

Saunders, 2002, Support vector machine, Computer Science, 1, 1

Breiman, 2001, Random forests, Machine Learning, 45, 5, 10.1023/A:1010933404324

Freund, 1999, A decision-theoretic generalization of on-line learning and an application to boosting, J Comput Syst Sci, 55, 119, 10.1006/jcss.1997.1504

Cheng, 2019, Research on travel time prediction model of freeway based on gradient boosting decision tree, IEEE Access, 7, 7466, 10.1109/ACCESS.2018.2886549

An, 2007, Fast cross-validation algorithms for least squares support vector machine and kernel ridge regression, Pattern Recognition, 40, 2154, 10.1016/j.patcog.2006.12.015

Cao, 2015, Conference on Information and Knowledge Management, 891

Le Cun Y, Fogelman-Soulie F, 1987, Modeles connexionnistes de l'apprentissage, 1

LeCun, 1989, Backpropagation applied to handwritten zip code recognition, Neural Comput, 1, 541, 10.1162/neco.1989.1.4.541

Kipf, 2017, International Conference on Learning Representations

Velickovic, 2018, International Conference on Learning Representations

Grover, 2016, Node2vec: scalable feature learning for networks, International Conference on Knowledge Discovery & Data Mining, 855

Wang, 2020, Research on improved text classification method based on combined weighted model, Concurrency and Computation-Practice & Experience, 32, e5140.1, 10.1002/cpe.5140

Kharazmi, 2017, Text coherence new method using word2vec sentence vectors and most likely n-grams, Conference on Signal Processing and Intelligent Systems Shahrood. Iran, 105

Perozzi, 2014, The 20th ACM International Conference on Knowledge Discovery and Data Mining, 701

Mikolov, 2013, Efficient estimation of word representations in vector space, Computer Science, arXiv

You, 2017, PBMDA: a novel and effective path-based computational model for miRNA-disease association prediction, PLoS Comput Biol, 13, 10.1371/journal.pcbi.1005455

Zhang, 2020, MSFSP: a novel mirna-disease association prediction model by federating multiple-similarities fusion and space projection, Front Genet, 11, 389, 10.3389/fgene.2020.00389

Yan, 2019, DNRLMF-MDA:predicting microRNA-disease associations based on similarities of microRNAs and diseases, IEEE ACM Trans Comput Biol Bioinform, 16, 233, 10.1109/TCBB.2017.2776101

Lan, 2018, Predicting microRNA-disease associations based on improved microrna and disease similarities, IEEE ACM Trans Comput Biol Bioinform, 15, 1774, 10.1109/TCBB.2016.2586190

Chen, 2018, Predicting miRNA-disease association based on inductive matrix completion, Bioinformatics, 34, 4256, 10.1093/bioinformatics/bty503

Ding, 2020, Variational graph auto-encoders for miRNA-disease association prediction, Methods, online, 10.1016/j.ymeth.2020.1008.1004

Ji, 2020, AEMDA: inferring miRNA-disease associations based on deep autoencoder, Bioinformatics, online

Sumathipala, 2020, Predicting miRNA-based disease-disease relationships through network diffusion on multi-omics biological data, Sci Rep, 10, 10.1038/s41598-020-65633-6

Ha, 2020, Improved prediction of miRNA-disease associations based on matrix completion with network regularization, Cell, 9, 881, 10.3390/cells9040881

Zheng, 2020, iCDA-CGR: identification of circrna-disease associations based on chaos game representation, PLoS Comput Biol, 16, e1007872, 10.1371/journal.pcbi.1007872

Ding, 2020, Deep belief network-based matrix factorization model for microrna-disease associations prediction, Evol Bioinform, 16, 10.1177/1176934320919707

Huang, 2020, Predicting microRNA-disease associations from lncRNA–microRNA interactions via multiview multitask learning, Brief Bioinform, online

Yao, 2020, IMDAILM: inferring miRNA-disease association by integrating lncRNA and miRNA data, IEEE Access, 8, 16517, 10.1109/ACCESS.2019.2958055

Jin, 2020, Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction, Bioinformatics, 36, 2538, 10.1093/bioinformatics/btz965

Zheng, 2019, You ZHJIA. CGMDA: An approach to predict and validate microRNA-disease associations by utilizing chaos game representation and lightGBM, IEEE Access, Online, 10.1109/ACCESS.2019.2940470

Xie, 2019, WBNPMD: weighted bipartite network projection for microRNA-disease association prediction, J Transl Med, 17, 322, 10.1186/s12967-019-2063-4

Zheng, 2019, MLMDA: a machine learning approach to predict and validate MicroRNA-disease associations by integrating of heterogenous information sources, J Transl Med, 17, 10.1186/s12967-019-2009-x

Chen, 2019, Identifying MiRNA-disease association based on integrating miRNA topological similarity and functional similarity, Quant Bio, 7, 202, 10.1007/s40484-019-0176-7

Zhang, 2019, A fast linear neighborhood similarity-based network link inference method to predict microRNA-disease associations, IEEE/ACM Trans Comput Biol Bioinform, online, 10.1109/TCBB.2019.2931546

Chen, 2020, Potential miRNA-disease association prediction based on kernelized Bayesian matrix factorization, Genomics, 112, 809, 10.1016/j.ygeno.2019.05.021

Ha, 2019, PMAMCA: prediction of microRNA-disease association utilizing a matrix completion approach, BMC Syst Biol, 13, 33, 10.1186/s12918-019-0700-4

Tang, 2019, Dual Laplacian regularized matrix completion for microRNA-disease associations prediction, RNA Biol, 16, 601, 10.1080/15476286.2019.1570811

Yu, 2019, Inferring microRNA-disease association by hybrid recommendation algorithm and unbalanced bi-random walk on heterogeneous network, Sci Rep, 9

Zhao, 2018, A novel approach based on a weighted interactive network to predict associations of miRNAs and diseases, Int J Mol Sci, 20, 110, 10.3390/ijms20010110

Chen, 2018, TLHNMDA: triple layer heterogeneous network based inference for MiRNA-disease association prediction, Front Genet, 9, 234, 10.3389/fgene.2018.00234

Peng, 2017, A framework for integrating multiple biological networks to predict MicroRNA-disease associations, IEEE Trans Nanobioscience, 16, 100, 10.1109/TNB.2016.2633276

Lu, 2020, Predicting human lncRNA-disease associations based on geometric matrix completion, IEEE J Biomed Health Inform, 24, 2420, 10.1109/JBHI.2019.2958389

Yao, 2020, A random forest based computational model for predicting novel lncRNA-disease associations, BMC Bioinformatics, 21, 10.1186/s12859-020-3458-1

Wu, 2020, Inferring LncRNA-disease associations based on graph autoencoder matrix completion, Comput Biol Chem, 87, 10.1016/j.compbiolchem.2020.107282

Wei, 2020, iLncRNAdis-FB: identify lncRNA-disease associations by fusing biological feature blocks through deep neural network, IEEE/ACM Trans Comput Biol Bioinform, online, 10.1109/tcbb.2020.2964221

Xuan, 2019, Graph convolutional network and convolutional neural network based method for predicting lncRNA-disease associations, Cell, 8, 1012, 10.3390/cells8091012

Wang, 2019, IDLDA: An improved diffusion model for predicting LncRNA-disease associations, Front Genet, 10, 1259, 10.3389/fgene.2019.01259

Li, 2019, NCPCDA: network consistency projection for circRNA-disease association prediction, RSC Adv, 9, 33222, 10.1039/C9RA06133A

Guo, 2019, A learning-based method for LncRNA-disease association identification combing similarity information and rotation Forest, iScience, 19, 786, 10.1016/j.isci.2019.08.030

Sumathipala, 2019, Network diffusion approach to predict lncRNA disease associations using multi-type biological networks: LION, Front Physiol, 10, 888, 10.3389/fphys.2019.00888

Ping, 2019, A novel method for lncRNA-disease association prediction based on an lncrna-disease association network, IEEE/ACM Trans Comput Biol Bioinform, 16, 688, 10.1109/TCBB.2018.2827373

Xuan, 2019, Dual convolutional neural networks with attention mechanisms based method for predicting disease-related lncRNA genes, Front Genet, 10, 416, 10.3389/fgene.2019.00416

Gu, 2017, Global network random walk for predicting potential human lncRNA-disease associations, Sci Rep, 7, 10.1038/s41598-017-12763-z

Zhou, 2014, Prioritizing candidate disease-related long non-coding RNAs by walking on the heterogeneous lncRNA and disease network, Mol Biosyst, 11, 760, 10.1039/C4MB00511B

Ganegoda, 2015, Heterogeneous network model to infer human disease-long intergenic non coding RNA associations, IEEE Trans Nanobioscience, 14, 175, 10.1109/TNB.2015.2391133

Tian, 2015, 2015 IEEE 12th International Conference on e-Business Engineering, 43, 10.1109/ICEBE.2015.17

Yan, 2018, DWNN-RLS: regularized least squares method for predicting circRNA-disease associations, BMC Bioinformatics, S19

Wei, 2019, iCircDA-MF: identification of circRNA-disease associations based on matrix factorization, Brief Bioinform, 21, 1356, 10.1093/bib/bbz057

Lei, 2019, GBDTCDA: predicting circRNA-disease associations based on gradient boosting decision tree with multiple biological data fusion, Int J Biol Sci, 15, 2911, 10.7150/ijbs.33806

Fan, 2020, Prioritizing CircRNA-disease associations with convolutional neural network based on multiple similarity feature fusion, Front Genet, 11, 540751, 10.3389/fgene.2020.540751

Wang, 2019, An efficient approach based on multi-sources information to predict circRNA-disease associations using deep convoltional neural network, Bioinformatics, 36, 4038, 10.1093/bioinformatics/btz825

Xiao, 2020, An in-silico method with graph-based multi-label learning for large-scale prediction of circRNA-disease associations, Genomics, 112, 3407, 10.1016/j.ygeno.2020.06.017

Ding, 2020, Predicting novel CircRNA-disease associations based on random walk and logistic regression model, Comput Biol Chem, 87, 10.1016/j.compbiolchem.2020.107287

Li, 2020, Prediction of circRNA-disease associations based on inductive matrix completion, BMC Med Genomics, 13, 10.1186/s12920-020-0679-0

Lei, 2020, Integrating random walk with restart and k-nearest neighbor to identify novel circRNA-disease association, Sci Rep, 10, 1943, 10.1038/s41598-020-59040-0

Lei, 2019, BRWSP: predicting circRNA-disease associations based on biased random walk to search paths on a multiple heterogeneous network, Complexity, 2019, 1, 10.1155/2019/5938035

Xiao, 2019, Computational prediction of human disease- associated circRNAs based on manifold regularization learning framework, IEEE J Biomed Health Inform, 23, 2661, 10.1109/JBHI.2019.2891779

Zhao, 2019, Integrating bipartite network projection and katz measure to identify novel circRNA-disease associations, IEEE Trans Nanobioscience, 18, 578, 10.1109/TNB.2019.2922214

Deng, 2019, Fusion of multiple heterogeneous networks for predicting circRNA-disease associations, Sci Rep, 9, 10.1038/s41598-019-45954-x

Lei, 2020, Predicting metabolite-disease associations based on linear Neighborhood similarity with improved bipartite network projection algorithm, Complexity, 2020, 1

Lei, 2020, Relational completion based non-negative matrix factorization for predicting metabolite-disease associations, Knowledge-Based Systems, 204, 10.1016/j.knosys.2020.106238

Fan, 2019, Predicting the associations between microbes and diseases by integrating multiple data sources and path-based HeteSim scores, Neurocomputing, 323, 76, 10.1016/j.neucom.2018.09.054

Lei, 2020, Predicting microbe-disease association by learning graph representations and rule-based inference on the heterogeneous network, Front Microbiol, 11, 579, 10.3389/fmicb.2020.00579

Liu, 2016, Neighborhood regularized logistic matrix factorization for drug-target interaction prediction, PLoS Comput Biol, 12, 10.1371/journal.pcbi.1004760

Öztürk, 2018, DeepDTA: deep drug-target binding affinity prediction, Bioinformatics, 34, i821, 10.1093/bioinformatics/bty593

Wang, 2019, Identifying cancer-specific circRNA-RBP binding sites based on deep learning, Molecules, 24, 4035, 10.3390/molecules24224035

Yi, 2020, Learning representations to predict intermolecular interactions on large-scale heterogeneous molecular association network, iScience, 23, 10.1016/j.isci.2020.101261