Negatif olmayan matris faktorizasyonuna dayalı LncRNA-Hastalık ilişkisi tahmini

Yıl 2023, Cilt: 12 Sayı: 4, 1194 - 1199, 15.10.2023

Ahmet Toprak

https://doi.org/10.28948/ngumuh.1279335

Öz

Yapılan birçok biyolojik deneylerde lncRNA’nın çeşitli karmaşık insan hastalıklarının gelişimi ile yakından ilişkili olduğunu kanıtlanmıştır. Bu nedenle lncRNA ve hastalık arasındaki ilişkiyi bilmek sadece hastalık mekanizmasını anlamaya yardımcı olmakla kalmaz, aynı zamanda hastalığın teşhisini, tedavisini ve prognozunu da kolaylaştırır. Fakat lncRNA ile hastalık arasındaki ilişkiyi biyolojik deneyler yoluyla belirlemek hem maliyetli hem de çok zaman gerektiren bir süreçtir. Bu sebeple birçok araştırmacı potansiyel lncRNA-hastalık ilişkilerini tahmin etmek için hesaplamalı yöntemler geliştirmişlerdir. Bu çalışmada, fonksiyonel olarak benzer lncRNA’ların fenotipik olarak benzer hastalıklarla ilişki kurma eğiliminde olduğu varsayımına dayanarak, potansiyel lncRNA’ları tahmin etmek için NMF adı verilen bir hesaplama yöntemi öneriyoruz. Bu yöntem lncRNA ekspresyon benzerlik ağını, lncRNA kosinüs benzerlik ağını, hastalık semantik benzerlik ağını, hastalık kosinüs benzerlik ağını ve bilinen lncRNA-hastalık ilişkilendirme ağını entegre etmektedir. Yöntemimizin tahmin doğruluğunu göstermek için 5-katlı çapraz doğrulama ve birini dışarıda bırak çapraz doğrulama tekniklerini uyguladık ve ROC grafiklerini elde ettik. 5-katlı çapraz doğrulama için 0.7837 AUC değeri, birini dışarıda bırak çapraz doğrulama için 0.8551 AUC değeri hesaplandı. Sonuçlar NMF yönteminin güvenilir tahmin performansına sahip olduğunu göstermektedir.

Anahtar Kelimeler

lncRNA, hastalık, lncRNA-hastalık ilişkisi, NMF

LncRNA-Disease association prediction based on nonnegative matrix factorization

Öz

Many biological experiments have proven that lncRNA is related to various complex human diseases. Therefore, knowing the lncRNA-disease relationships not only facilitates the diagnosis, treatment and prognosis of the disease helps to understand the disease mechanism. However, determining the lncRNA-disease relationships through biological experiments is both costly and time-consuming. For this reason, many researchers have suggested calculational methods to forecast potential relationships between lncRNAs and diseases. In this study, we suggest a computational method named NMF to forecast possible lncRNAs, based on the assumption that functionally similar lncRNAs tend to associate with phenotypically similar diseases. This method integrates the lncRNA expression similarity network, the lncRNA cosine similarity network, the disease semantic similarity network, the disease cosine similarity network, and the known lncRNA-disease relationship network. To demonstrate the prediction accuracy of our method, we applied 5-fold cross-validation and leave-out cross-validation techniques and obtained ROC plots. AUC of 0.7837 for 5-fold cross-validation and 0.8551 AUC for leave-out cross-validation were calculated. The results show that the NMF method has reliable prediction performance.

Anahtar Kelimeler

lncRNA, disease, lncRNA-disease association, NMF

Kaynakça

• Washietl, S., M. Kellis, and M. Garber, Evolutionary dynamics and tissue specificity of human long noncoding RNAs in six mammals. Genome research, 2014. 24(4): p. 616-628. DOI: https://doi.org/10.1101/gr.165035.113.
• Schmitt, A.M. and H.Y. Chang, Long noncoding RNAs in cancer pathways. Cancer cell, 2016. 29(4): p. 452-463. DOI: https://doi.org/10.1016/j.ccell.2016.03.010.
• Huang, H., et al., Upregulation of LncRNA PANDAR predicts poor prognosis and promotes cell proliferation in cervical cancer. Eur Rev Med Pharmacol Sci, 2017. 21(20): p. 4529-4535.
• 4. Zhu, Y., et al., Oncogenic activity of Wrap53 in human colorectal cancer in vitro and in nude mouse xenografts. Medical Science Monitor: International Medical Journal of Experimental and Clinical Research, 2018. 24: p. 6129. DOI: https://doi.org/10.12659/MSM.910214.
• Tripathi, M.K., et al., Role of lncRNAs in ovarian cancer: defining new biomarkers for therapeutic purposes. Drug Discovery Today, 2018. 23(9): p. 1635-1643. DOI: https://doi.org/10.1016/j.drudis.2018.04.010.
• Sun, S.-C., et al., Expression of long non-coding RNA H19 in prostate cancer and its effect on the proliferation and glycometabolism of human prostate cancer cells. Zhonghua nan ke xue= National Journal of Andrology, 2017. 23(2): p. 120-124.
• Thomas, A.A., et al., lncRNA H19 prevents endothelial–mesenchymal transition in diabetic retinopathy. Diabetologia, 2019. 62: p. 517-530. DOI: https://doi.org/10.1007/s00125-018-4797-6.
• Collette, J., X. Le Bourhis, and E. Adriaenssens, Regulation of human breast cancer by the long non-coding RNA H19. International journal of molecular sciences, 2017. 18(11): p. 2319. DOI: https://doi.org/10.3390/ijms18112319.
• Iacoangeli, A., et al., BC200 RNA in invasive and preinvasive breast cancer. Carcinogenesis, 2004. 25(11): p. 2125-2133. DOI: https://doi.org/10.1093/carcin/bgh228.
• Pasmant, E., et al., Characterization of a germ-line deletion, including the entire INK4/ARF locus, in a melanoma-neural system tumor family: identification of ANRIL, an antisense noncoding RNA whose expression coclusters with ARF. Cancer research, 2007. 67(8): p. 3963-3969. DOI: https://doi.org/10.1158/0008-5472.CAN-06-2004.
• Gao, Y., 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 research, 2021. 49(D1): p. D1251-D1258. DOI: https://doi.org/10.1093/nar/gkaa1006.
• Cui, T., et al., MNDR v2. 0: an updated resource of ncRNA–disease associations in mammals. Nucleic acids research, 2018. 46(D1): p. D371-D374. DOI: https://doi.org/10.1093/nar/gkx1025.
• Dinger, M.E., et al., NRED: a database of long noncoding RNA expression. Nucleic acids research, 2009. 37(suppl_1): p. D122-D126. DOI: https://doi.org/10.1093/nar/gkn617.
• Chen, G., et al., LncRNADisease: a database for long-non-coding RNA-associated diseases. Nucleic acids research, 2012. 41(D1): p. D983-D986. DOI: https://doi.org/10.1093/nar/gks1099.
• Zhang, X., et al., Long non-coding RNA expression profiles predict clinical phenotypes in glioma. Neurobiology of disease, 2012. 48(1): p. 1-8. DOI: https://doi.org/10.1016/j.nbd.2012.06.004.
• 16. Schriml, L.M., et al., Disease Ontology: a backbone for disease semantic integration. Nucleic acids research, 2012. 40(D1): p. D940-D946. DOI: https://doi.org/10.1093/nar/gkr972.
• Parkinson, H., et al., ArrayExpress—a public database of microarray experiments and gene expression profiles. Nucleic Acids Research, 2006. 35(suppl_1): p. D747-D750. DOI: https://doi.org/10.1093/nar/gkl995.
• Chen, X. and G.-Y. Yan, Novel human lncRNA–disease association inference based on lncRNA expression profiles. Bioinformatics, 2013. 29(20): p. 2617-2624. DOI: https://doi.org/10.1093/bioinformatics/btt426.
• Toprak, A. and E. Eryilmaz, Prediction of miRNA-disease associations based on Weighted K-Nearest known neighbors and network consistency projection. Journal of Bioinformatics and Computational Biology, 2021. 19(1): p. 2050041. DOI: https://doi.org/10.1142/S0219720020500419.
• Toprak, A. and E. Eryilmaz Dogan, Prediction of Potential MicroRNA-Disease Association Using Kernelized Bayesian Matrix Factorization. Interdisciplinary Sciences: Computational Life Sciences, 2021. 13(4): p. 595-602. DOI: https://doi.org/10.1007/s12539-021-00469-w.
• Luo, X., et al., An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Transactions on Industrial Informatics, 2014. 10(2): p. 1273-1284. DOI: https://doi.org/10.1109/TII.2014.2308433.
• Zhang, W., et al., Manifold regularized matrix factorization for drug-drug interaction prediction. Journal of biomedical informatics, 2018. 88: p. 90-97. DOI: https://doi.org/10.1016/j.jbi.2018.11.005.
• Zhang, T., et al., LPGNMF: predicting long non-coding RNA and protein interaction using graph regularized nonnegative matrix factorization. IEEE/ACM transactions on computational biology and bioinformatics, 2018. 17(1): p. 189-197. DOI: https://doi.org/10.1109/TCBB.2018.2861009.
• Xiao, Q., et al., A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations. Bioinformatics, 2018. 34(2): p. 239-248. DOI: https://doi.org/10.1093/bioinformatics/btx545.
• Liu, Y., S.-L. Wang, and J.-F. Zhang, Prediction of microbe–disease associations by graph regularized non-negative matrix factorization. Journal of Computational Biology, 2018. 25(12): p. 1385-1394. DOI: https://doi.org/10.1089/cmb.2018.0072.
• Zhang, W., et al., Predicting drug-disease associations by using similarity constrained matrix factorization. BMC bioinformatics, 2018. 19: p. 1-12. DOI: https://doi.org/10.1186/s12859-018-2220-4.
• Wei, H. and B. Liu, iCircDA-MF: identification of circRNA-disease associations based on matrix factorization. Briefings in bioinformatics, 2020. 21(4): p. 1356-1367. DOI: https://doi.org/10.1093/bib/bbz057.
• Lee, D.D. and H.S. Seung, Learning the parts of objects by non-negative matrix factorization. Nature, 1999. 401(6755): p. 788-791. DOI: https://doi.org/10.1038/44565.
• Fawcett, T., An introduction to ROC analysis. Pattern Recognition Letters, 2006. 27(8): p. 861-874. DOI: https://doi.org/10.1016/j.patrec.2005.10.010.
• Yu, G., et al., BRWLDA: bi-random walks for predicting lncRNA-disease associations. Oncotarget, 2017. 8(36): p. 60429. DOI: https://doi.org/10.18632/oncotarget.19588.
• Sun, J., et al., Inferring novel lncRNA–disease associations based on a random walk model of a lncRNA functional similarity network. Molecular BioSystems, 2014. 10(8): p. 2074-2081. DOI: https://doi.org/10.1039/c3mb70608g.
• Chen, X., KATZLDA: KATZ measure for the lncRNA-disease association prediction. Scientific Reports, 2015. 5: p. 16840. DOI: https://doi.org/10.1038/srep16840.