Prediction of disease-related metabolites using bi-random walks
Autoři:
Xiujuan Lei aff001; Jiaojiao Tie aff001
Působiště autorů:
School of Computer Science, Shaanxi Normal University, Xi’an China
aff001
Vyšlo v časopise:
PLoS ONE 14(11)
Kategorie:
Research Article
doi:
https://doi.org/10.1371/journal.pone.0225380
Souhrn
Metabolites play a significant role in various complex human disease. The exploration of the relationship between metabolites and diseases can help us to better understand the underlying pathogenesis. Several network-based methods have been used to predict the association between metabolite and disease. However, some methods ignored hierarchical differences in disease network and failed to work in the absence of known metabolite-disease associations. This paper presents a bi-random walks based method for disease-related metabolites prediction, called MDBIRW. First of all, we reconstruct the disease similarity network and metabolite functional similarity network by integrating Gaussian Interaction Profile (GIP) kernel similarity of diseases and GIP kernel similarity of metabolites, respectively. Then, the bi-random walks algorithm is executed on the reconstructed disease similarity network and metabolite functional similarity network to predict potential disease-metabolite associations. At last, MDBIRW achieves reliable performance in leave-one-out cross validation (AUC of 0.910) and 5-fold cross validation (AUC of 0.924). The experimental results show that our method outperforms other existing methods for predicting disease-related metabolites.
Klíčová slova:
Alzheimer's disease – Colorectal cancer – Drug metabolism – Metabolic networks – Metabolites – Obesity – Random walk – Semantics
Zdroje
1. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, et al. Metabolite profiles and the risk of developing diabetes. Nature medicine. 2011;17(4):448. doi: 10.1038/nm.2307 21423183
2. Cheng L, Yang H, Zhao H, Pei X, Shi H, Sun J, et al. MetSigDis: a manually curated resource for the metabolic signatures of diseases. Briefings in bioinformatics. 2017;20(1):203–9.
3. Lee D-S, Park J, Kay K, Christakis NA, Oltvai Z, Barabási A-L. The implications of human metabolic network topology for disease comorbidity. Proceedings of the National Academy of Sciences. 2008;105(29):9880–5.
4. Dong H, Li J, Huang L, Chen X, Li D, Wang T, et al. Serum microRNA profiles serve as novel biomarkers for the diagnosis of Alzheimer’s disease. Disease markers. 2015;2015.
5. Chen X, Xie D, Wang L, Zhao Q, You Z-H, Liu H. BNPMDA: bipartite network projection for MiRNA–disease association prediction. Bioinformatics. 2018;34(18):3178–86. doi: 10.1093/bioinformatics/bty333 29701758
6. Yan C, Wang J, Wu F-X. DWNN-RLS: regularized least squares method for predicting circRNA-disease associations. BMC bioinformatics. 2018;19(19):520.
7. Luo H, Wang J, Li M, Luo J, Peng X, Wu F-X, et al. Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm. Bioinformatics. 2016;32(17):2664–71. doi: 10.1093/bioinformatics/btw228 27153662
8. Yan C, Wang J, Ni P, Lan W, Wu F-X, Pan Y. DNRLMF-MDA: predicting microRNA-disease associations based on similarities of microRNAs and diseases. IEEE/ACM transactions on computational biology and bioinformatics. 2017;16(1):233–43. doi: 10.1109/TCBB.2017.2776101 29990253
9. Czech C, Berndt P, Busch K, Schmitz O, Wiemer J, Most V, et al. Metabolite Profiling of Alzheimer's Disease Cerebrospinal Fluid. PLOS ONE. 2012;7(2):e31501. doi: 10.1371/journal.pone.0031501 22359596
10. Pieragostino D, D'Alessandro M, Di Ioia M, Rossi C, Zucchelli M, Urbani A, et al. An integrated metabolomics approach for the research of new cerebrospinal fluid biomarkers of multiple sclerosis. Molecular bioSystems. 2015;11(6):1563–72. doi: 10.1039/c4mb00700j 25690641
11. Moats RA, Ernst T, Shonk TK, Ross BD. Abnormal cerebral metabolite concentrations in patients with probable Alzheimer disease. Magnetic resonance in medicine. 1994;32(1):110–5. doi: 10.1002/mrm.1910320115 8084225
12. Ebbel EN, Leymarie N, Schiavo S, Sharma S, Gevorkian S, Hersch S, et al. Identification of phenylbutyrate-generated metabolites in Huntington disease patients using parallel liquid chromatography/electrochemical array/mass spectrometry and off-line tandem mass spectrometry. Analytical biochemistry. 2010;399(2):152–61. doi: 10.1016/j.ab.2010.01.010 20074541
13. Connor SC, Hansen MK, Corner A, Smith RF, Ryan TE. Integration of metabolomics and transcriptomics data to aid biomarker discovery in type 2 diabetes. Molecular BioSystems. 2010;6(5):909–21. doi: 10.1039/b914182k 20567778
14. Baumgartner C, Spath-Blass V, Niederkofler V, Bergmoser K, Langthaler S, Lassnig A, et al. A novel network-based approach for discovering dynamic metabolic biomarkers in cardiovascular disease. PLOS ONE. 2018;13(12):e0208953. doi: 10.1371/journal.pone.0208953 30533038
15. Shang D, Li C, Yao Q, Yang H, Xu Y, Han J, et al. Prioritizing candidate disease metabolites based on global functional relationships between metabolites in the context of metabolic pathways. PloS one. 2014;9(8):e104934. doi: 10.1371/journal.pone.0104934 25153931
16. Hu Y, Zhao T, Zhang N, Zang T, Zhang J, Cheng L. Identifying diseases-related metabolites using random walk. BMC bioinformatics. 2018;19(5):116.
17. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, et al. HMDB 3.0—the human metabolome database in 2013. Nucleic acids research. 2012;41(D1):D801–D7.
18. Kibbe WA, Arze C, Felix V, Mitraka E, Bolton E, Fu G, et al. Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic acids research. 2014;43(D1):D1071–D8.
19. Lowe HJ, Barnett GO. Understanding and using the medical subject headings (MeSH) vocabulary to perform literature searches. Jama. 1994;271(14):1103–8. 8151853
20. 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. doi: 10.1093/bioinformatics/btq241 20439255.
21. van Laarhoven T, Nabuurs SB, Marchiori E. Gaussian interaction profile kernels for predicting drug–target interaction. Bioinformatics. 2011;27(21):3036–43. doi: 10.1093/bioinformatics/btr500 21893517
22. Yu G, Fu G, Lu C, Ren Y, Wang J. BRWLDA: bi-random walks for predicting lncRNA-disease associations. Oncotarget. 2017;8(36):60429. doi: 10.18632/oncotarget.19588 28947982
23. Yu G, Fu G, Wang J, Zhao Y. NewGOA: Predicting new GO annotations of proteins by bi-random walks on a hybrid graph. IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB). 2018;15(4):1390–402.
24. Zou Q, Li J, Wang C, Zeng X. Approaches for recognizing disease genes based on network. BioMed research international. 2014;2014.
25. Niu Y-W, Liu H, Wang G-H, Yan G-Y. Maximal entropy random walk on heterogenous network for MIRNA-disease Association prediction. Mathematical biosciences. 2018;306:1–9. doi: 10.1016/j.mbs.2018.10.004 30336146
26. Tong H, Faloutsos C, Pan J-Y, editors. Fast random walk with restart and its applications. Sixth International Conference on Data Mining (ICDM'06); 2006: IEEE.
27. Wang C, Feng R, Sun D, Li Y, Bi X, Sun C. Metabolic profiling of urine in young obese men using ultra performance liquid chromatography and Q-TOF mass spectrometry (UPLC/Q-TOF MS). Journal of Chromatography B. 2011;879(27):2871–6.
28. Zhao H, Shen J, Djukovic D, Daniel‐MacDougall C, Gu H, Wu X, et al. Metabolomics‐identified metabolites associated with body mass index and prospective weight gain among Mexican American women. Obesity science & practice. 2016;2(3):309–17.
29. Liu L, Feng R, Guo F, Li Y, Jiao J, Sun C. Targeted metabolomic analysis reveals the association between the postprandial change in palmitic acid, branched-chain amino acids and insulin resistance in young obese subjects. Diabetes research and clinical practice. 2015;108(1):84–93. doi: 10.1016/j.diabres.2015.01.014 25700627
30. Kaur S, Birdsill AC, Steward K, Pasha E, Kruzliak P, Tanaka H, et al. Higher visceral fat is associated with lower cerebral N-acetyl-aspartate ratios in middle-aged adults. Metabolic brain disease. 2017;32(3):727–33. doi: 10.1007/s11011-017-9961-z 28144886
31. Weinberg BA, Marshall JL, Salem ME. The growing challenge of young adults with colorectal cancer. Oncology. 2017;31(5).
32. Ni Y, Xie G, Jia W. Metabonomics of human colorectal cancer: new approaches for early diagnosis and biomarker discovery. Journal of proteome research. 2014;13(9):3857–70. doi: 10.1021/pr500443c 25105552
33. Lane CA, Hardy J, Schott JM. Alzheimer's disease. European Journal of Neurology. 2018;25(1):59–70. doi: 10.1111/ene.13439 28872215
34. Ibáñez C, Simó C, Martín-Álvarez PJ, Kivipelto M, Winblad B, Cedazo-Mínguez A, et al. Toward a predictive model of Alzheimer’s disease progression using capillary electrophoresis–mass spectrometry metabolomics. Analytical chemistry. 2012;84(20):8532–40. doi: 10.1021/ac301243k 22967182
35. González-Domínguez R, García-Barrera T, Gómez-Ariza JL. Combination of metabolomic and phospholipid-profiling approaches for the study of Alzheimer's disease. Journal of proteomics. 2014;104:37–47. doi: 10.1016/j.jprot.2014.01.014 24473279
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