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Kernelized Bayesian Matris Faktorizasyonu ile mikroRNA-Hastalık İlişkilerinin Tanımlanması ve Analizi

Year 2021, Issue: 28, 40 - 45, 30.11.2021
https://doi.org/10.31590/ejosat.980257

Abstract

Birçok farklı hastalığın başlamasında ve ilerlemesinde etkili olan mikroRNA (miRNA) molekülleri yaklaşık 22 nükleotid uzunluğunda kodla yapmayan bir RNA türüdür. Bilim insanları karmaşık insan hastalıklarının önlenmesi, teşhisi ve tedavisinde miRNA’ların önemini açıklamıştır. Bu nedenle son yıllarda araştırmacılar potansiyel miRNA-hastalık ilişkilerini bulmak için çok çalışmaktalar. miRNA’lar ve hastalıklar arasında yeni ilişkiler bulmak için kullanılan deneysel tekniklerin zaman alıcı ve pahalı olması nedeniyle birçok hesaplama tekniği geliştirilmiştir. Bu çalışmada yeni miRNA-hastalık ilişkilerini tahmin etmek için Kernelized Bayesian matrix factorization (KBMF) tekniğini önerdik. 5-katlı çapraz doğrulama tekniği uyguladık ve 0.9450 ortalama AUC değeri elde ettik. Ayrıca KBMF tekniğinin performansını kanıtlamak için meme, akciğer ve kolon neoplazmalarına dayalı vaka çalışmaları uyguladık. Sonuçlar KBMF’nin olası miRNA-hastalık ilişkilerini ortaya çıkarmak için güvenilir bir hesaplama modeli olarak kullanılabileceğini gösterdi.

References

  • Al-Hajj, M., Wicha, M. S., Benito-Hernandez, A., Morrison, S. J., & Clarke, M. F. (2003). Prospective identification of tumorigenic breast cancer cells. Proceedings of the National Academy of Sciences, 100(7), 3983-3988. doi:10.1073/pnas.0530291100
  • Alexiou, P., Vergoulis, T., Gleditzsch, M., Prekas, G., Dalamagas, T., Megraw, M., . . . Hatzigeorgiou, A. G. (2009). miRGen 2.0: a database of microRNA genomic information and regulation. Nucleic Acids Research, 38(suppl_1), D137-D141. doi:10.1093/nar/gkp888
  • Ammad-Ud-Din, M., Georgii, E., Gonen, M., Laitinen, T., Kallioniemi, O., Wennerberg, K., . . . Kaski, S. (2014). Integrative and personalized QSAR analysis in cancer by kernelized Bayesian matrix factorization. Journal of chemical information and modeling, 54(8), 2347-2359. doi:10.1021/ci500152b
  • Bartel, D. P. (2009). MicroRNAs: target recognition and regulatory functions. cell, 136(2), 215-233. doi:10.1016/j.cell.2009.01.002
  • Chen, X. (2015). KATZLDA: KATZ measure for the lncRNA-disease association prediction. Scientific reports, 5, 16840. doi:10.1038/srep16840
  • Chen, X., & Huang, L. (2017). LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction. PLoS Computational Biology, 13(12), e1005912. doi:10.1371/journal.pcbi.1005912
  • Chen, X., Huang, L., Xie, D., & Zhao, Q. (2018). EGBMMDA: Extreme Gradient Boosting Machine for MiRNA-Disease Association prediction. Cell Death & Disease, 9(1), 3. doi:10.1038/s41419-017-0003-x
  • Chen, X., Huang, Y.-A., Wang, X.-S., You, Z.-H., & Chan, K. C. (2016). FMLNCSIM: fuzzy measure-based lncRNA functional similarity calculation model. Oncotarget, 7(29), 45948. doi:10.18632/oncotarget.10008
  • Chen, X., Wang, L. Y., & Huang, L. (2018). NDAMDA: Network distance analysis for MiRNA-disease association prediction. Journal of Cellular and Molecular Medicine, 22(5), 2884-2895. doi:10.1111/jcmm.13583
  • Chen, X., Wu, Q. F., & Yan, G. Y. (2017). RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction. RNA biology, 14(7), 952-962. doi:10.1080/15476286.2017.1312226
  • Chen, X., Xie, D., Wang, L., Zhao, Q., You, Z. H., & Liu, H. (2018). BNPMDA: Bipartite Network Projection for MiRNA-Disease Association prediction. Bioinformatics, 34(18), 3178-3186. doi:10.1093/bioinformatics/bty333
  • Chen, X., Yan, C. C., Zhang, X., You, Z. H., Deng, L., Liu, Y., . . . Dai, Q. (2016). WBSMDA: Within and Between Score for MiRNA-Disease Association prediction. Scientific reports, 6, 21106. doi:10.1038/srep21106
  • Chen, X., Zhou, Z., & Zhao, Y. (2018). ELLPMDA: ensemble learning and link prediction for miRNA-disease association prediction. RNA biology, 15(6), 807-818. doi:10.1080/15476286.2018.1460016
  • Drusco, A., Nuovo, G. J., Zanesi, N., Di Leva, G., Pichiorri, F., Volinia, S., . . . Bottoni, A. (2014). MicroRNA profiles discriminate among colon cancer metastasis. PLoS One, 9(6), e96670. doi:10.1371/journal.pone.0096670
  • Espinosa, C. E. S., & Slack, F. J. (2006). Cancer issue: the role of microRNAs in cancer. The Yale journal of biology and medicine, 79(3-4), 131-140.
  • Gönen, M., Khan, S., & Kaski, S. (2013). Kernelized Bayesian matrix factorization. Paper presented at the International Conference on Machine Learning.
  • Jiang, Q., Hao, Y., Wang, G., Juan, L., Zhang, T., Teng, M., . . . Wang, Y. (2010). Prioritization of disease microRNAs through a human phenome-microRNAome network. BMC Systems Biology, 4(1), S2. doi:10.1186/1752-0509-4-s1-s2
  • Jiang, Q., Wang, Y., Hao, Y., Juan, L., Teng, M., Zhang, X., . . . Liu, Y. (2009). miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res, 37(Database issue), D98-104. doi:10.1093/nar/gkn714
  • Kim, Y.-K. (2015). Extracellular microRNAs as biomarkers in human disease. Chonnam medical journal, 51(2), 51-57. doi:10.4068/cmj.2015.51.2.51
  • Kozomara, A., & Griffiths-Jones, S. (2013). miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Research, 42(D1), D68-D73. doi:10.1093/nar/gkt1181
  • Lan, W., Wang, J., Li, M., Liu, J., Wu, F. X., & Pan, Y. (2018). Predicting MicroRNA-Disease Associations Based on Improved MicroRNA and Disease Similarities. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(6), 1774-1782. doi:10.1109/TCBB.2016.2586190
  • Li, J.-Q., Rong, Z.-H., Chen, X., Yan, G.-Y., & You, Z.-H. (2017). MCMDA: Matrix completion for MiRNA-disease association prediction. Oncotarget, 8(13), 21187. doi:10.18632/oncotarget.15061
  • Li, Y., Qiu, C., Tu, J., Geng, B., Yang, J., Jiang, T., & Cui, Q. (2014). HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Research, 42(Database issue), D1070-D1074. doi:10.1093/nar/gkt1023
  • Mugunga, I., Ju, Y., Liu, X., & Huang, X. (2017). Computational prediction of human disease-related microRNAs by path-based random walk. Oncotarget, 8(35), 58526. doi:10.18632/oncotarget.17226
  • Ogata-Kawata, H., Izumiya, M., Kurioka, D., Honma, Y., Yamada, Y., Furuta, K., . . . Sonoda, H. (2014). Circulating exosomal microRNAs as biomarkers of colon cancer. PLoS One, 9(4), e92921. doi:10.1371/journal.pone.0092921
  • Phipps, A. I., Lindor, N. M., Jenkins, M. A., Baron, J. A., Win, A. K., Gallinger, S., . . . Newcomb, P. A. (2013). Colon and rectal cancer survival by tumor location and microsatellite instability: the Colon Cancer Family Registry. Diseases of the colon and rectum, 56(8), 937. doi:10.1097/DCR.0b013e31828f9a57
  • Shao, B., Liu, B., & Yan, C. (2018). SACMDA: MiRNA-Disease Association Prediction with Short Acyclic Connections in Heterogeneous Graph. Neuroinformatics, 16(3-4), 373-382. doi:10.1007/s12021-018-9373-1
  • Tang, C., Zhou, H., Zheng, X., Zhang, Y., & Sha, X. (2019). Dual Laplacian regularized matrix completion for microRNA-disease associations prediction. RNA biology, 16(5), 601-611. doi:10.1080/15476286.2019.1570811
  • Toprak, A., & Eryilmaz, E. (2020). Prediction of miRNA-disease associations based on Weighted K-Nearest known neighbors and network consistency projection. Journal of Bioinformatics and Computational Biology, 18(6), 2050041. doi:10.1142/s0219720020500419
  • Torre, L. A., Bray, F., Siegel, R. L., Ferlay, J., Lortet‐Tieulent, J., & Jemal, A. (2015). Global cancer statistics, 2012. CA: a cancer journal for clinicians, 65(2), 87-108. doi:10.3322/caac.21262
  • van Laarhoven, T., Nabuurs, S. B., & Marchiori, E. (2011). Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics, 27(21), 3036-3043. doi:10.1093/bioinformatics/btr500
  • Wang, C. C., Chen, X., Yin, J., & Qu, J. (2019). An integrated framework for the identification of potential miRNA-disease association based on novel negative samples extraction strategy. RNA biology, 16(3), 257-269. doi:10.1080/15476286.2019.1568820
  • Wang, D., Wang, J., Lu, M., Song, F., & Cui, Q. (2010). Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics, 26(13), 1644-1650. doi:10.1093/bioinformatics/btq241
  • Xuan, P., Han, K., Guo, M., Guo, Y., Li, J., Ding, J., . . . Huang, Y. (2013). Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors. PLoS One, 8(8), e70204. doi:10.1371/journal.pone.0070204
  • Yang, J.-H., Shao, P., Zhou, H., Chen, Y.-Q., & Qu, L.-H. (2009). deepBase: a database for deeply annotating and mining deep sequencing data. Nucleic Acids Research, 38(suppl_1), D123-D130. doi:10.1093/nar/gkp943
  • Yang, Z., Ren, F., Liu, C., He, S., Sun, G., Gao, Q., . . . Zhao, H. (2010). dbDEMC: a database of differentially expressed miRNAs in human cancers. BMC Genomics, 11 Suppl 4, S5. doi:10.1186/1471-2164-11-S4-S5
  • Yu, H., Chen, X., & Lu, L. (2017). Large-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm. Scientific reports, 7, 43792. doi:10.1038/srep43792

Identification and Analysis of microRNA-Disease Associations with Kernelized Bayesian Matrix Factorization

Year 2021, Issue: 28, 40 - 45, 30.11.2021
https://doi.org/10.31590/ejosat.980257

Abstract

MicroRNA (miRNA) molecules, which are effective on the initiation and progression of many different diseases, are a type of non-coding RNA with a length of about 22 nucleotides. Scientists have reported the importance of miRNAs in the prevention, diagnosis, and treatment of complex human diseases. Therefore, in the last decade, researchers have been working hard to find potential miRNA-disease associations. Many computational techniques have been developed because of the experimental techniques are time-consuming and expensive used to find new relationships between miRNAs and diseases. In this study, we suggested Kernelized Bayesian matrix factorization (KBMF) technique to predict new miRNA-disease relationships. We applied 5-fold cross validation technique and obtained an average value AUC of 0.9450. Also, we applied case studies based on breast, lung, and colon neoplasms to prove the performance of KBMF technique. The results showed that KBMF can be used as a reliable computational model to reveal possible miRNA-disease relationships.

References

  • Al-Hajj, M., Wicha, M. S., Benito-Hernandez, A., Morrison, S. J., & Clarke, M. F. (2003). Prospective identification of tumorigenic breast cancer cells. Proceedings of the National Academy of Sciences, 100(7), 3983-3988. doi:10.1073/pnas.0530291100
  • Alexiou, P., Vergoulis, T., Gleditzsch, M., Prekas, G., Dalamagas, T., Megraw, M., . . . Hatzigeorgiou, A. G. (2009). miRGen 2.0: a database of microRNA genomic information and regulation. Nucleic Acids Research, 38(suppl_1), D137-D141. doi:10.1093/nar/gkp888
  • Ammad-Ud-Din, M., Georgii, E., Gonen, M., Laitinen, T., Kallioniemi, O., Wennerberg, K., . . . Kaski, S. (2014). Integrative and personalized QSAR analysis in cancer by kernelized Bayesian matrix factorization. Journal of chemical information and modeling, 54(8), 2347-2359. doi:10.1021/ci500152b
  • Bartel, D. P. (2009). MicroRNAs: target recognition and regulatory functions. cell, 136(2), 215-233. doi:10.1016/j.cell.2009.01.002
  • Chen, X. (2015). KATZLDA: KATZ measure for the lncRNA-disease association prediction. Scientific reports, 5, 16840. doi:10.1038/srep16840
  • Chen, X., & Huang, L. (2017). LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction. PLoS Computational Biology, 13(12), e1005912. doi:10.1371/journal.pcbi.1005912
  • Chen, X., Huang, L., Xie, D., & Zhao, Q. (2018). EGBMMDA: Extreme Gradient Boosting Machine for MiRNA-Disease Association prediction. Cell Death & Disease, 9(1), 3. doi:10.1038/s41419-017-0003-x
  • Chen, X., Huang, Y.-A., Wang, X.-S., You, Z.-H., & Chan, K. C. (2016). FMLNCSIM: fuzzy measure-based lncRNA functional similarity calculation model. Oncotarget, 7(29), 45948. doi:10.18632/oncotarget.10008
  • Chen, X., Wang, L. Y., & Huang, L. (2018). NDAMDA: Network distance analysis for MiRNA-disease association prediction. Journal of Cellular and Molecular Medicine, 22(5), 2884-2895. doi:10.1111/jcmm.13583
  • Chen, X., Wu, Q. F., & Yan, G. Y. (2017). RKNNMDA: Ranking-based KNN for MiRNA-Disease Association prediction. RNA biology, 14(7), 952-962. doi:10.1080/15476286.2017.1312226
  • Chen, X., Xie, D., Wang, L., Zhao, Q., You, Z. H., & Liu, H. (2018). BNPMDA: Bipartite Network Projection for MiRNA-Disease Association prediction. Bioinformatics, 34(18), 3178-3186. doi:10.1093/bioinformatics/bty333
  • Chen, X., Yan, C. C., Zhang, X., You, Z. H., Deng, L., Liu, Y., . . . Dai, Q. (2016). WBSMDA: Within and Between Score for MiRNA-Disease Association prediction. Scientific reports, 6, 21106. doi:10.1038/srep21106
  • Chen, X., Zhou, Z., & Zhao, Y. (2018). ELLPMDA: ensemble learning and link prediction for miRNA-disease association prediction. RNA biology, 15(6), 807-818. doi:10.1080/15476286.2018.1460016
  • Drusco, A., Nuovo, G. J., Zanesi, N., Di Leva, G., Pichiorri, F., Volinia, S., . . . Bottoni, A. (2014). MicroRNA profiles discriminate among colon cancer metastasis. PLoS One, 9(6), e96670. doi:10.1371/journal.pone.0096670
  • Espinosa, C. E. S., & Slack, F. J. (2006). Cancer issue: the role of microRNAs in cancer. The Yale journal of biology and medicine, 79(3-4), 131-140.
  • Gönen, M., Khan, S., & Kaski, S. (2013). Kernelized Bayesian matrix factorization. Paper presented at the International Conference on Machine Learning.
  • Jiang, Q., Hao, Y., Wang, G., Juan, L., Zhang, T., Teng, M., . . . Wang, Y. (2010). Prioritization of disease microRNAs through a human phenome-microRNAome network. BMC Systems Biology, 4(1), S2. doi:10.1186/1752-0509-4-s1-s2
  • Jiang, Q., Wang, Y., Hao, Y., Juan, L., Teng, M., Zhang, X., . . . Liu, Y. (2009). miR2Disease: a manually curated database for microRNA deregulation in human disease. Nucleic Acids Res, 37(Database issue), D98-104. doi:10.1093/nar/gkn714
  • Kim, Y.-K. (2015). Extracellular microRNAs as biomarkers in human disease. Chonnam medical journal, 51(2), 51-57. doi:10.4068/cmj.2015.51.2.51
  • Kozomara, A., & Griffiths-Jones, S. (2013). miRBase: annotating high confidence microRNAs using deep sequencing data. Nucleic Acids Research, 42(D1), D68-D73. doi:10.1093/nar/gkt1181
  • Lan, W., Wang, J., Li, M., Liu, J., Wu, F. X., & Pan, Y. (2018). Predicting MicroRNA-Disease Associations Based on Improved MicroRNA and Disease Similarities. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 15(6), 1774-1782. doi:10.1109/TCBB.2016.2586190
  • Li, J.-Q., Rong, Z.-H., Chen, X., Yan, G.-Y., & You, Z.-H. (2017). MCMDA: Matrix completion for MiRNA-disease association prediction. Oncotarget, 8(13), 21187. doi:10.18632/oncotarget.15061
  • Li, Y., Qiu, C., Tu, J., Geng, B., Yang, J., Jiang, T., & Cui, Q. (2014). HMDD v2.0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Research, 42(Database issue), D1070-D1074. doi:10.1093/nar/gkt1023
  • Mugunga, I., Ju, Y., Liu, X., & Huang, X. (2017). Computational prediction of human disease-related microRNAs by path-based random walk. Oncotarget, 8(35), 58526. doi:10.18632/oncotarget.17226
  • Ogata-Kawata, H., Izumiya, M., Kurioka, D., Honma, Y., Yamada, Y., Furuta, K., . . . Sonoda, H. (2014). Circulating exosomal microRNAs as biomarkers of colon cancer. PLoS One, 9(4), e92921. doi:10.1371/journal.pone.0092921
  • Phipps, A. I., Lindor, N. M., Jenkins, M. A., Baron, J. A., Win, A. K., Gallinger, S., . . . Newcomb, P. A. (2013). Colon and rectal cancer survival by tumor location and microsatellite instability: the Colon Cancer Family Registry. Diseases of the colon and rectum, 56(8), 937. doi:10.1097/DCR.0b013e31828f9a57
  • Shao, B., Liu, B., & Yan, C. (2018). SACMDA: MiRNA-Disease Association Prediction with Short Acyclic Connections in Heterogeneous Graph. Neuroinformatics, 16(3-4), 373-382. doi:10.1007/s12021-018-9373-1
  • Tang, C., Zhou, H., Zheng, X., Zhang, Y., & Sha, X. (2019). Dual Laplacian regularized matrix completion for microRNA-disease associations prediction. RNA biology, 16(5), 601-611. doi:10.1080/15476286.2019.1570811
  • Toprak, A., & Eryilmaz, E. (2020). Prediction of miRNA-disease associations based on Weighted K-Nearest known neighbors and network consistency projection. Journal of Bioinformatics and Computational Biology, 18(6), 2050041. doi:10.1142/s0219720020500419
  • Torre, L. A., Bray, F., Siegel, R. L., Ferlay, J., Lortet‐Tieulent, J., & Jemal, A. (2015). Global cancer statistics, 2012. CA: a cancer journal for clinicians, 65(2), 87-108. doi:10.3322/caac.21262
  • van Laarhoven, T., Nabuurs, S. B., & Marchiori, E. (2011). Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics, 27(21), 3036-3043. doi:10.1093/bioinformatics/btr500
  • Wang, C. C., Chen, X., Yin, J., & Qu, J. (2019). An integrated framework for the identification of potential miRNA-disease association based on novel negative samples extraction strategy. RNA biology, 16(3), 257-269. doi:10.1080/15476286.2019.1568820
  • Wang, D., Wang, J., Lu, M., Song, F., & Cui, Q. (2010). Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics, 26(13), 1644-1650. doi:10.1093/bioinformatics/btq241
  • Xuan, P., Han, K., Guo, M., Guo, Y., Li, J., Ding, J., . . . Huang, Y. (2013). Prediction of microRNAs associated with human diseases based on weighted k most similar neighbors. PLoS One, 8(8), e70204. doi:10.1371/journal.pone.0070204
  • Yang, J.-H., Shao, P., Zhou, H., Chen, Y.-Q., & Qu, L.-H. (2009). deepBase: a database for deeply annotating and mining deep sequencing data. Nucleic Acids Research, 38(suppl_1), D123-D130. doi:10.1093/nar/gkp943
  • Yang, Z., Ren, F., Liu, C., He, S., Sun, G., Gao, Q., . . . Zhao, H. (2010). dbDEMC: a database of differentially expressed miRNAs in human cancers. BMC Genomics, 11 Suppl 4, S5. doi:10.1186/1471-2164-11-S4-S5
  • Yu, H., Chen, X., & Lu, L. (2017). Large-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm. Scientific reports, 7, 43792. doi:10.1038/srep43792
There are 37 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Ahmet Toprak 0000-0003-3337-4917

Esma Eryılmaz Doğan 0000-0001-6809-7513

Publication Date November 30, 2021
Published in Issue Year 2021 Issue: 28

Cite

APA Toprak, A., & Eryılmaz Doğan, E. (2021). Identification and Analysis of microRNA-Disease Associations with Kernelized Bayesian Matrix Factorization. Avrupa Bilim Ve Teknoloji Dergisi(28), 40-45. https://doi.org/10.31590/ejosat.980257