Abstract
In this study, we propose a manifold learning-based method for predicting disease genes by assuming that a disease and its associated genes should be consistent in some lower dimensional manifold. The 10-fold cross-validation experiments show that the area under of the receiver operating characteristic (ROC) curve (AUC) generated by our approach is 0.7452 with high-quality gene-disease associations in OMIM dataset, which is greater that of the competing method PBCF (0.5700). 9 out of top 10 predicted gene-disease associations can be supported by existing literature, which is better than the result (6 out of top 10 predicted association) of the PBCF. All these results illustrate that our method outperforms the competing method.
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References
Glaab, E., Bacardit, J., Garibaldi, J.M., Natalio, K.: Using rule-based machine learning for candidate disease gene prioritization and sample classification of cancer gene expression data. PLoS ONE 7(7), e39932 (2012)
Isakov, O., Dotan, I., Ben-Shachar, S.: Machine learningbased gene prioritization identifies novel candidate risk genes for inflammatory bowel disease. Inflamm. Bowel Dis. 23(9), 15161523 (2017)
Mordelet, F., Vert, J.P.: Prodige: prioritization of disease genes with multitask machine learning from positive and unlabeled examples. BMC Bioinf. 12, 389 (2011)
Chen, B., Wu, F.X.: Identifying protein complexes based on multiple topological structures in PPI networks. IEEE Trans. Nanobiosci. 12(3), 165–172 (2016)
Chen, B., Li, M., Wang, J., Wu, F.X.: Disease gene identification by using graph kernels and Markov random fields. Sci. China Life Sci. 57(11), 1054–1063 (2014)
Chen, B., Wang, J., Li, M., Wu, F.X.: Identifying disease genes by integrating multiple data sources. BMC Med. Genomics 7(2), S2 (2014)
Chen, B., Li, M., Wang, J., Shang, X., Wu, F.X.: A fast and high performance multiple data integration algorithm for identifying human disease genes. BMC Med. Genomicse 8(3), S2 (2015)
Chen, B., Shang, X., Li, M., Wang, J., Wu, F.X.: Identifying individual-cancer-related genes by rebalancing the training samples. IEEE Trans. Nanobiosci. 15(4), 309–315 (2016)
Luo, P., Tian, L.P., Ruan, J., Wu, F.X.: Disease gene prediction by integrating PPI networks, clinical RNA-Seq data and OMIM data. IEEE/ACM Trans. Comput. Biol. Bioinf. (2017, in press). https://doi.org/10.1109/TCBB.2017.2770120
Natarajan, N., Dhillon, I.S.: Inductive matrix completion for predicting genedisease associations. Bioinformatics 30(12), i60–i68 (2014)
Zeng, X., Ding, N., Rodrguez-Patn, A., Zou, Q.: Probability-based collaborative filtering model for predicting genedisease associations. BMC Med. Genomics 10(S5), 76 (2017)
Li, L., Wu, L., Zhang, H., Wu, F.X.: A fast algorithm for nonnegative matrix factorization and its convergence. IEEE Trans. Neural Netw. Learn. Syst. 25(10), 1855–1863 (2014)
Li-Ping, T., Luo, P., Wang, H., Huiru, Z., Wu, F.X.: CASNMF: a converged algorithm for symmetrical nonnegative matrix factorization. Neurocomputing 275, 2031–2040 (2018)
Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput. 15(6), 1373–1396 (2003)
Ham, J., Lee, D.D., Saul, L.K.: Semisupervised alignment of manifolds. In: AISTATS, pp. 120–127 (2005)
Amberger, J.S., Bocchini, C.A., Schiettecatte, F., Scott, A.F., Hamosh, A.: OMIM. org: online mendelian inheritance in man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 43(D1), D789–D798 (2014)
Jolliffe, I.T.: Principal Component Analysis. Springer, New York (2002). https://doi.org/10.1007/b98835
Greenacre, M.J.: Theory and Applications of Correspondence Analysis. Academic Press, New York (1984)
Acknowledgments
This work is supported in part by Natural Science and Engineering Research Council of Canada (NSERC), China Scholarship Council (CSC) and by the National Natural Science Foundation of China under Grant No. 61772552 and 61571052.
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Luo, P., Tian, LP., Chen, B., Xiao, Q., Wu, FX. (2018). Predicting Gene-Disease Associations with Manifold Learning. In: Zhang, F., Cai, Z., Skums, P., Zhang, S. (eds) Bioinformatics Research and Applications. ISBRA 2018. Lecture Notes in Computer Science(), vol 10847. Springer, Cham. https://doi.org/10.1007/978-3-319-94968-0_26
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DOI: https://doi.org/10.1007/978-3-319-94968-0_26
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