Abstract
Essential proteins are indispensable for cell survival, and the identification of essential proteins plays a critical role in biological and pharmaceutical design research. Recently, some machine learning methods have been proposed by introducing effective protein features or by employing powerful classifiers. Seldom of them focused on improving the prediction accuracy by designing efficient strategies to ensemble different classifiers. In this work, a novel ensemble learning framework called by Tri-ensemble was proposed to integrate different classifiers, which selected three weak classifiers and trained these classifiers by continually adding the samples that are predicted to have abnormally high or abnormally low properties by the other two classifiers. We applied Tri-ensemble on predicting the essential protein of Yeast and E.coli. The results show that our approach achieves better performance than both individual classifiers and the other ensemble learning methods.
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References
Ren, Z., Yan, L.: DEG 50, a database of essential genes in both prokaryotes and eukaryotes. Nucleic Acids Res. 37(Database issue), D455 (2009)
Jeong, H., Mason, S.P., Barabasi, A.L., Oltvai, Z.N.: Lethality and centrality in protein networks. Nature 411(6833), 41–42 (2001)
Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40(1), 35–41 (1977)
Joy, M.P., Brock, A., Ingber, D.E., Huang, S.: High-betweenness proteins in the yeast protein interaction network. J. Biomed. Biotechnol. 2005(2), 96 (2014)
Stefan, W., Stadler, P.F.: Centers of complex networks. J. Theor. Biol. 223(1), 45–53 (2003)
Vallabhajosyula, R.R., Deboki, C., Samina, L., Animesh, R., Alpan, R.: Identifying hubs in protein interaction networks. PLoS ONE 4(4), e5344 (2009)
Bonacich, P.: Power and centrality: a family of measures. Am. J. Sociol. 92(5), 1170–1182 (1987)
Stephenson, K., Zelen, M.: Rethinking centrality: methods and examples ☆. Soc. Netw. 11(1), 1–37 (1989)
Wang, J., Li, M., Wang, H., Pan, Y.: Identification of essential proteins based on edge clustering coefficient. IEEE/ACM Trans. Comput. Biol. Bioinf. 9(4), 1070–1080 (2012)
Ernesto, E., RodrÃguez-Velázquez, J.A.: Subgraph centrality in complex networks. Phys. Rev. E Stat. Nonlinear Soft Matter Phys. 71(5 Pt 2), 056103 (2005)
Li, M., Zhang, H., Fei, Y.: Essential protein discovery method based on integration of PPI and gene expression data. J. Cent. South Univ. 44(3), 1024–1029 (2013)
Tang, X., Wang, J., Yi, P.: Identifying essential proteins via integration of protein interaction and gene expression data (2012)
Jordan, I.K., Rogozin, I.B., Wolf, Y.I., Koonin, E.V.: Essential genes are more evolutionarily conserved than are nonessential genes in bacteria. Genome Res. 12(6), 962 (2002)
Hart, G.T., Lee, I., Marcotte, E.M.: A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality. BMC Bioinform. 8(1), 1–11 (2007)
Peng, W., Wang, J., Wang, W., Liu, Q., Wu, F.X., Pan, Y.: Iteration method for predicting essential proteins based on orthology and protein-protein interaction networks. BMC Syst. Biol. 6(1), 1–17 (2012)
Gustafson, A.M., Snitkin, E.S., Parker, S.C., Delisi, C., Kasif, S.: Towards the identification of essential genes using targeted genome sequencing and comparative analysis. BMC Genom. 7(1), 265 (2006)
Hwang, Y.C., Lin, C.C., Chang, J.Y., Mori, H., Juan, H.F., Huang, H.C.: Predicting essential genes based on network and sequence analysis. Mol. BioSyst. 5(12), 1672–1678 (2009)
Zhong, J., Wang, J., Peng, W., Zhang, Z., Pan, Y.: Prediction of essential proteins based on gene expression programming. BMC Genom. 14(S4), S7 (2013)
Acencio, M.L., Lemke, N.: Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information. BMC Bioinform. 10(1), 290 (2009)
Deng, J., et al.: Investigating the predictability of essential genes across distantly related organisms using an integrative approach. Nucleic Acids Res. 39(3), 795–807 (2011)
Chen, Y., Xu, D.: Understanding protein dispensability through machine-learning analysis of high-throughput data. Bioinformatics 21(5), 575–581 (2005)
Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123–140 (1996)
Schapire, R.E., Singer, Y., Singhal, A.: Boosting and Rocchio applied to text filtering. In: SIGIR Proceedings of Annual International Conference on Research & Development in Information Retrieval, pp. 215–223 (1998)
Chen, T., Guestrin, C.: XGBoost: a scalable tree boosting system (2016)
Breiman, L.: Stacked regressions. Mach. Learn. 24(1), 49–64 (1996)
Li, M., Zhou, Z.-H.: Tri-training exploiting unlabeled data using three classifiers. IEEE Trans. Knowl. Data Eng. 17(11), 1529–1541 (2005)
Mewes, F.D., et al.: MIPS: analysis and annotation of proteins from whole genomes in 2005. Nucleic Acids Res. 34(Database issue), 169–172 (2004)
Cherry, J.M., et al.: SGD: saccharomyces genome database. Nucleic Acids Res. 26(1), 73–79 (1998)
Saccharomyces Genome Deletion Project. http://www-sequence.stanford.edu/group/yeast_deletion_project/deletions3.html
Xenarios, I., Salwinski, L., Duan, X.J., Higney, P., Kim, S.M., Eisenberg, D.: DIP, the database of interacting proteins: a research tool for studying cellular networks of protein interactions. Nucleic Acids Res. 30(1), 303 (2002)
Tang, Y., Li, M., Wang, J., Pan, Y., Wu, F.X.: CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems 127, 67–72 (2015)
Gabriel, O., et al.: InParanoid 7: new algorithms and tools for eukaryotic orthology analysis. Nucleic Acids Res. 38(Database issue), D196 (2010)
Tu, B.P., Andrzej, K., Maga, R., Mcknight, S.L.: Logic of the yeast metabolic cycle: temporal compartmentalization of cellular processes. Science 310(5751), 1152 (2005)
Andea, P., Pier Luigi, M., Piero, F., Rita, C.: eSLDB: eukaryotic subcellular localization database. Nucl. Acids Res. 35(Database issue), 208–212 (2007)
Acknowledgment
This work is supported in part by the National Natural Science Foundation of China under grant No. 31560317, No. 61502214, No. 61502166, No. 61702122 and No. 81560221. Natural Science Foundation of Yunnan Province of China (No. 2016FB107).
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Dai, W., Li, X., Peng, W., Song, J., Zhong, J., Wang, J. (2019). Improving Identification of Essential Proteins by a Novel Ensemble Method. In: Cai, Z., Skums, P., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2019. Lecture Notes in Computer Science(), vol 11490. Springer, Cham. https://doi.org/10.1007/978-3-030-20242-2_13
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DOI: https://doi.org/10.1007/978-3-030-20242-2_13
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