Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

DHOSGR: lncRNA-disease Association Prediction Based on Decay High-order Similarity and Graph-regularized Matrix Completion

Author(s): Guobo Xie, Zelin Jiang, Zhiyi Lin*, Guosheng Gu, Yuping Sun, Qing Su, Ji Cui and Huizhe Zhang

Volume 18, Issue 1, 2023

Published on: 23 December, 2022

Page: [92 - 104] Pages: 13

DOI: 10.2174/1574893618666221118092849

Price: $65

Abstract

Background: It has been shown in numerous recent studies that long non-coding RNAs (lncRNAs) play a vital role in the regulation of various biological processes, as well as serve as a basis for understanding the causes of human illnesses. Thus, many researchers have developed matrix completion approaches to infer lncRNA–disease connections and enhance prediction performance by using similarity information.

Objective: Most matrix completion approaches are solely based on the first-order or second-order similarity between nodes, and higher-order similarity is rarely considered. In view of this, we developed a computational method to incorporate higher-order similarity information into the similarity network with different weights using a decay function designed by a random walk with restart (DHOSGR).

Methods: First, considering that the information will decay as the distance increases during network propagation, we defined a novel decay high-order similarity by combining the similarity matrix and its high-order similarity information through a decay function to construct a similarity network. Then, we applied the similarity network to the objective function as a graph regularization term. Finally, a proximal splitting algorithm was used to perform matrix completion to infer relationships between diseases and lncRNAs.

Results: In the experiment, DHOSGR achieves a superior performance in leave-one-out cross validation (LOOCV) and 100 times 5-fold cross validation (5-fold-CV), with AUC values of 0.9459 and 0.9334 ± 0.0016, respectively, which are better than other five previous models. Moreover, case studies of three diseases (leukemia, lymphoma, and squamous cell carcinoma) demonstrated that DHOSGR can reliably predict associated lncRNAs.

Conclusion: DHOSGR can serve as a high efficiency calculation model for predicting lncRNAdisease associations.

« Previous Next »
Graphical Abstract

[1]
Ponting CP, Oliver PL, Reik W. Evolution and functions of long noncoding RNAs. Cell 2009; 136(4): 629-41.
[http://dx.doi.org/10.1016/j.cell.2009.02.006] [PMID: 19239885]
[2]
Esteller M. Non-coding RNAs in human disease. Nat Rev Genet 2011; 12(12): 861-74.
[http://dx.doi.org/10.1038/nrg3074] [PMID: 22094949]
[3]
Wapinski O, Chang HY. Long noncoding RNAs and human disease. Trends Cell Biol 2011; 21(6): 354-61.
[http://dx.doi.org/10.1016/j.tcb.2011.04.001] [PMID: 21550244]
[4]
Rinn JL, Chang HY. Genome regulation by long noncoding RNAs. Annu Rev Biochem 2012; 81(1): 145-66.
[http://dx.doi.org/10.1146/annurev-biochem-051410-092902] [PMID: 22663078]
[5]
Geisler S, Coller J. RNA in unexpected places: Long non-coding RNA functions in diverse cellular contexts. Nat Rev Mol Cell Biol 2013; 14(11): 699-712.
[http://dx.doi.org/10.1038/nrm3679] [PMID: 24105322]
[6]
Chen Q, Li G, Phoebe Chen YP. Interval-based distance function for identifying RNA structure candidates. J Theor Biol 2011; 269(1): 280-6.
[http://dx.doi.org/10.1016/j.jtbi.2010.11.002] [PMID: 21056578]
[7]
Chen X, Sun YZ, Guan NN, et al. Computational models for lncRNA function prediction and functional similarity calculation. Brief Funct Genomics 2019; 18(1): 58-82.
[http://dx.doi.org/10.1093/bfgp/ely031] [PMID: 30247501]
[8]
Tsai MC, Manor O, Wan Y, et al. Long noncoding RNA as modular scaffold of histone modification complexes. Science 2010; 329(5992): 689-93.
[http://dx.doi.org/10.1126/science.1192002] [PMID: 20616235]
[9]
Gupta RA, Shah N, Wang KC, et al. Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature 2010; 464(7291): 1071-6.
[http://dx.doi.org/10.1038/nature08975] [PMID: 20393566]
[10]
Powell WT, Coulson RL, Crary FK, et al. A Prader–Willi locus lncRNA cloud modulates diurnal genes and energy expenditure. Hum Mol Genet 2013; 22(21): 4318-28.
[http://dx.doi.org/10.1093/hmg/ddt281] [PMID: 23771028]
[11]
Chen G, Wang Z, Wang D, et al. LncRNADisease: A database for long-non-coding RNA-associated diseases. Nucleic Acids Res 2013; 41: D983-6.
[PMID: 23175614]
[12]
Ning S, Zhang J, Wang P, et al. Lnc2Cancer: A manually curated database of experimentally supported lncRNAs associated with various human cancers. Nucleic Acids Res 2016; 44(D1): D980-5.
[http://dx.doi.org/10.1093/nar/gkv1094] [PMID: 26481356]
[13]
Chen X, Yan CC, Zhang X, You ZH. Long non-coding RNAs and complex diseases: From experimental results to computational models. Brief Bioinform 2017; 18(4): 558-76.
[PMID: 27345524]
[14]
Jalali S, Kapoor S, Sivadas A, Bhartiya D, Scaria V. Computational approaches towards understanding human long non-coding RNA biology. Bioinformatics 2015; 31(14): 2241-51.
[http://dx.doi.org/10.1093/bioinformatics/btv148] [PMID: 25777523]
[15]
Ou-Yang L, Huang J, Zhang XF, et al. lncRNA-disease association prediction using two-side sparse self-representation. Front Genet 2019; 10: 476.
[http://dx.doi.org/10.3389/fgene.2019.00476] [PMID: 31191605]
[16]
Chen X, Wang CC, Yin J, You ZH. Novel human miRNA-disease association inference based on random forest. Mol Ther Nucleic Acids 2018; 13: 568-79.
[http://dx.doi.org/10.1016/j.omtn.2018.10.005] [PMID: 30439645]
[17]
Chen X. Predicting lncRNA-disease associations and constructing lncRNA functional similarity network based on the information of miRNA. Sci Rep 2015; 5(1): 13186.
[http://dx.doi.org/10.1038/srep13186] [PMID: 26278472]
[18]
Chen X. KATZLDA: KATZ measure for the lncRNA-disease association prediction. Sci Rep 2015; 5(1): 16840.
[http://dx.doi.org/10.1038/srep16840] [PMID: 26577439]
[19]
Wang L, Xiao Y, Li J, Feng X, Li Q, Yang J. IIRWR: Internal inclined random walk with restart for lncRNA-disease association prediction. IEEE Access 2019; 7: 54034-41.
[http://dx.doi.org/10.1109/ACCESS.2019.2912945]
[20]
Xie G, Wu C, Gu G, Huang B. HAUBRW: Hybrid algorithm and unbalanced bi-random walk for predicting lncRNA-disease associations. Genomics 2020; 112(6): 4777-87.
[http://dx.doi.org/10.1016/j.ygeno.2020.08.024] [PMID: 33348478]
[21]
Zhang L, Yang P, Feng H, Zhao Q, Liu H. Using network distance analysis to predict lncRNA–miRNA interactions. Interdiscip Sci 2021; 13(3): 535-45.
[http://dx.doi.org/10.1007/s12539-021-00458-z] [PMID: 34232474]
[22]
Li J, Zhao H, Xuan Z, Yu J, Feng X, Liao B. A novel approach for potential human lncRNA-disease association prediction based on local random walk. IEEE/ACM Trans Comput Biol Bioinform 2019; 18(3): 1049-59.
[23]
Wang L, You ZH, Chen X, et al. LMTRDA: Using logistic model tree to predict MiRNA-disease associations by fusing multi-source information of sequences and similarities. PLOS Comput Biol 2019; 15(3): e1006865.
[http://dx.doi.org/10.1371/journal.pcbi.1006865] [PMID: 30917115]
[24]
Chen X, Zhu CC, Yin J. Ensemble of decision tree reveals potential miRNA-disease associations. PLOS Comput Biol 2019; 15(7): e1007209.
[http://dx.doi.org/10.1371/journal.pcbi.1007209] [PMID: 31329575]
[25]
Wang CC, Li TH, Huang L, Chen X. Prediction of potential miRNA–disease associations based on stacked autoencoder. Brief Bioinform 2022; 23(2): bbac021.
[http://dx.doi.org/10.1093/bib/bbac021] [PMID: 35176761]
[26]
Lan W, Lai D, Chen Q, et al. LDICDL: LncRNA-disease association identification based on collaborative deep learning. IEEE/ACM Trans Comput Biol Bioinformatics 2022; 19(3): 1715-23.
[http://dx.doi.org/10.1109/TCBB.2020.3034910] [PMID: 33125333]
[27]
Chen X, Yin J, Qu J, Huang L. MDHGI: Matrix decomposition and heterogeneous graph inference for miRNA-disease association prediction. PLOS Comput Biol 2018; 14(8): e1006418.
[http://dx.doi.org/10.1371/journal.pcbi.1006418] [PMID: 30142158]
[28]
Li W, Wang S, Xu J, Mao G, Tian G, Yang J. Inferring latent disease-lncRNA associations by faster matrix completion on a heterogeneous network. Front Genet 2019; 10: 769.
[http://dx.doi.org/10.3389/fgene.2019.00769] [PMID: 31572428]
[29]
Lu C, Yang M, Li M, Li Y, Wu FX, Wang J. Predicting human lncRNA-disease associations based on geometric matrix completion. IEEE J Biomed Health Inform 2020; 24(8): 2420-9.
[http://dx.doi.org/10.1109/JBHI.2019.2958389] [PMID: 31825885]
[30]
Gao MM, Cui Z, Gao YL, Wang J, Liu JX. Multi-label fusion collaborative matrix factorization for predicting lncRNA-Disease Associations. IEEE J Biomed Health Inform 2021; 25(3): 881-90.
[http://dx.doi.org/10.1109/JBHI.2020.2988720] [PMID: 32324583]
[31]
Xie G, Zhu Y, Lin Z, et al. HOPMCLDA: Predicting lncRNA–disease associations based on high-order proximity and matrix completion. Mol Omics 2021; 17(5): 760-8.
[http://dx.doi.org/10.1039/D1MO00138H] [PMID: 34251001]
[32]
Bao Z, Yang Z, Huang Z, Zhou Y, Cui Q, Dong D. LncRNADisease 2.0: An updated database of long non-coding RNA-associated diseases. Nucleic Acids Res 2019; 47(D1): D1034-7.
[http://dx.doi.org/10.1093/nar/gky905] [PMID: 30285109]
[33]
Gao Y, Shang S, Guo S, et al. Lnc2Cancer 3.0: An updated resource for experimentally supported lncRNA/circRNA cancer associations and web tools based on RNA-seq and scRNA-seq data. Nucleic Acids Res 2021; 49(D1): D1251-8.
[http://dx.doi.org/10.1093/nar/gkaa1006] [PMID: 33219685]
[34]
Schriml LM, Arze C, Nadendla S, et al. Disease Ontology: A backbone for disease semantic integration. Nucleic Acids Res 2012; 40(D1): D940-6.
[http://dx.doi.org/10.1093/nar/gkr972] [PMID: 22080554]
[35]
Wang D, Wang J, Lu M, Song F, Cui Q. Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics 2010; 26(13): 1644-50.
[http://dx.doi.org/10.1093/bioinformatics/btq241] [PMID: 20439255]
[36]
Zhao Y, Chen X, Yin J. A novel computational method for the identification of potential miRNA-disease association based on symmetric non-negative matrix factorization and Kronecker regularized least square. Front Genet 2018; 9: 324.
[http://dx.doi.org/10.3389/fgene.2018.00324] [PMID: 30186308]
[37]
Brazma A, Parkinson H, Sarkans U, et al. ArrayExpress - a public repository for microarray gene expression data at the EBI. Nucleic Acids Res 2003; 31(1): 68-71.
[http://dx.doi.org/10.1093/nar/gkg091] [PMID: 12519949]
[38]
Lan W, Li M, Zhao K, et al. LDAP: A web server for lncRNA-disease association prediction. Bioinformatics 2017; 33(3): 458-60.
[PMID: 28172495]
[39]
van Laarhoven T, Nabuurs SB, Marchiori E. Gaussian interaction profile kernels for predicting drug–target interaction. Bioinformatics 2011; 27(21): 3036-43.
[http://dx.doi.org/10.1093/bioinformatics/btr500] [PMID: 21893517]
[40]
You ZH, Huang ZA, Zhu Z, et al. PBMDA: A novel and effective path-based computational model for miRNA-disease association prediction. PLOS Comput Biol 2017; 13(3): e1005455.
[http://dx.doi.org/10.1371/journal.pcbi.1005455] [PMID: 28339468]
[41]
Zhang Z, Cui P, Wang X, Pei J, Yao X, Zhu W. Arbitrary-order proximity preserved network embedding. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2778-86.
[http://dx.doi.org/10.1145/3219819.3219969]
[42]
Cao S, Lu W, Xu Q. GraRep: Learning graph representations with global structural information. Proceedings of the 24th ACM international on conference on information and knowledge management. 891-900.
[http://dx.doi.org/10.1145/2806416.2806512]
[43]
Tong H, Faloutsos C, Pan JY. Random walk with restart: Fast solutions and applications. Knowl Inf Syst 2008; 14(3): 327-46.
[http://dx.doi.org/10.1007/s10115-007-0094-2]
[44]
Franceschini A, Lin J, von Mering C, Jensen LJ. SVD-phy: Improved prediction of protein functional associations through singular value decomposition of phylogenetic profiles. Bioinformatics 2016; 32(7): 1085-7.
[http://dx.doi.org/10.1093/bioinformatics/btv696] [PMID: 26614125]
[45]
Chen X, Huang L. LRSSLMDA: Laplacian regularized sparse subspace learning for MiRNA-disease association prediction. PLOS Comput Biol 2017; 13(12): e1005912.
[http://dx.doi.org/10.1371/journal.pcbi.1005912] [PMID: 29253885]
[46]
Combettes PL, Pesquet JC. Proximal splitting methods in signal processing. In: Fixed-point algorithms for inverse problems in science and engineering. Springer 2011; pp. 185-212.
[http://dx.doi.org/10.1007/978-1-4419-9569-8_10]
[47]
Tng SS, Le NQK, Yeh HY, Chua MCH. Improved prediction model of protein lysine crotonylation sites using bidirectional recurrent neural networks. J Proteome Res 2022; 21(1): 265-73.
[http://dx.doi.org/10.1021/acs.jproteome.1c00848] [PMID: 34812044]
[48]
Le NQK, Ho QT. Deep transformers and convolutional neural network in identifying DNA N6-methyladenine sites in cross-species genomes. Methods 2022; 204: 199-206.
[http://dx.doi.org/10.1016/j.ymeth.2021.12.004] [PMID: 34915158]
[49]
Hung TNK, Le NQK, Le NH, et al. An AI-based prediction model for drug-drug interactions in osteoporosis and Paget’s diseases from SMILES. Mol Inform 2022; 41(6): 2100264.
[http://dx.doi.org/10.1002/minf.202100264] [PMID: 34989149]
[50]
Xie G, Meng T, Luo Y, Liu Z. SKF-LDA: Similarity Kernel fusion for predicting lncRNA-disease association. Mol Ther Nucleic Acids 2019; 18: 45-55.
[http://dx.doi.org/10.1016/j.omtn.2019.07.022] [PMID: 31514111]
[51]
Sun J, Shi H, Wang Z, et al. Inferring novel lncRNA–disease associations based on a random walk model of a lncRNA functional similarity network. Mol Biosyst 2014; 10(8): 2074-81.
[http://dx.doi.org/10.1039/C3MB70608G] [PMID: 24850297]
[52]
Yu G, Fu G, Lu C, Ren Y, Wang J. BRWLDA: bi-random walks for predicting lncRNA-disease associations. Oncotarget 2017; 8(36): 60429-46.
[http://dx.doi.org/10.18632/oncotarget.19588] [PMID: 28947982]
[53]
Liu JX, Gao MM, Cui Z, Gao YL, Li F. DSCMF: Prediction of LncRNA-disease associations based on dual sparse collaborative matrix factorization. BMC Bioinformatics 2021; 22(S3): 241.
[http://dx.doi.org/10.1186/s12859-020-03868-w] [PMID: 33980147]
[54]
Wang X, Sehgal L, Jain N, Khashab T, Mathur R, Samaniego F. LncRNA MALAT1 promotes development of mantle cell lymphoma by associating with EZH2. J Transl Med 2016; 14(1): 346.
[http://dx.doi.org/10.1186/s12967-016-1100-9] [PMID: 27998273]
[55]
Fan CB, Yan XH, Tian M, et al. Long non-coding RNA NEAT1 regulates Hodgkin’s lymphoma cell proliferation and invasion via miR-448 mediated regulation of DCLK1. Eur Rev Med Pharmacol Sci 2020; 24(11): 6219-27.
[PMID: 32572888]
[56]
Zhu Q, Li Y, Guo Y, et al. Long non-coding RNA SNHG16 promotes proliferation and inhibits apoptosis of diffuse large B-cell lymphoma cells by targeting miR-497-5p/PIM1 axis. J Cell Mol Med 2019; 23(11): 7395-405.
[http://dx.doi.org/10.1111/jcmm.14601] [PMID: 31483572]
[57]
Doose G, Haake A, Bernhart SH, et al. MINCR is a MYC-induced lncRNA able to modulate MYC’s transcriptional network in Burkitt lymphoma cells. Proc Natl Acad Sci USA 2015; 112(38): E5261-70.
[http://dx.doi.org/10.1073/pnas.1505753112] [PMID: 26351698]
[58]
Zhang B, Sun YF, Zhang XM, Jiang N, Chen Q. TUG1 weakens the sensitivity of acute myeloid leukemia cells to cytarabine by regulating miR-655-3p/CCND1 axis. Eur Rev Med Pharmacol Sci 2020; 24(9): 4940-53.
[PMID: 32432757]
[59]
Gan S, Ma P, Ma J, et al. Knockdown of ZFAS1 suppresses the progression of acute myeloid leukemia by regulating microRNA-150/Sp1 and microRNA-150/Myb pathways. Eur J Pharmacol 2019; 844: 38-48.
[http://dx.doi.org/10.1016/j.ejphar.2018.11.036] [PMID: 30502345]
[60]
Yu Y, Kou D, Liu B, et al. LncRNA MEG3 contributes to drug resistance in acute myeloid leukemia by positively regulating ALG9 through sponging miR-155. Int J Lab Hematol 2020; 42(4): 464-72.
[http://dx.doi.org/10.1111/ijlh.13225] [PMID: 32359033]
[61]
Wang G, Li X, Song L, Pan H, Jiang J, Sun L. Long noncoding RNA MIAT promotes the progression of acute myeloid leukemia by negatively regulating miR-495. Leuk Res 2019; 87: 106265.
[http://dx.doi.org/10.1016/j.leukres.2019.106265] [PMID: 31698307]
[62]
Zheng X, Zhao K, Liu T, Liu L, Zhou C, Xu M. Long noncoding RNA PVT1 promotes laryngeal squamous cell carcinoma development by acting as a molecular sponge to regulate miR-519d-3p. J Cell Biochem 2019; 120(3): 3911-21.
[http://dx.doi.org/10.1002/jcb.27673] [PMID: 30304557]
[63]
Zhao YQ, Liu XB, Xu H, Liu S, Wang JM. MEG3 inhibits cell proliferation, invasion and epithelial-mesenchymal transition in laryngeal squamous cell carcinoma. Eur Rev Med Pharmacol Sci 2019; 23(5): 2062-8.
[PMID: 30915750]
[64]
Cao X, Luan K, Yang J, Huang Y. Targeting lncRNA PSMA3-AS1, a prognostic marker, suppresses malignant progression of oral squamous cell carcinoma. Dis Markers 2021; 2021: 3138046.
[65]
Li Z, Qin X, Bian W, et al. Exosomal lncRNA ZFAS1 regulates esophageal squamous cell carcinoma cell proliferation, invasion, migration and apoptosis via microRNA-124/STAT3 axis. J Exp Clin Cancer Res 2019; 38(1): 477.
[http://dx.doi.org/10.1186/s13046-019-1473-8] [PMID: 31775815]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy