Abstract
In the protein-protein interactions, hub proteins are the key factor to maintain stability of protein-protein interactions and exert the protein biological function. Conventional methods mainly focus on the topological structure and gene expression of hub proteins, while we mainly discuss the hot spots of hub protein interfaces. In order to evaluate the performance of the classification models, the importance of feature variables is analyzed by using the average precision descent curve and the average Gini coefficient descent curve. In addition, the margin box-plot is used to measure the certainty of the classification models. The experimental results show that the error rate of random forest method is lower, and our classification model has higher reliability.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Keskin, O., Tuncbag, N., et al.: Predicting protein-protein interactions from the molecular to the proteome level. Chem. Rev. 116(8), 4884–4909 (2016)
Scott, D.E., Bayly, A.R., et al.: Small molecules, big targets: drug discovery faces the protein-protein interaction challenge. Nat. Rev. Drug Discovery 15(8), 533–550 (2016)
Huang, D.S., Zhang, L., et al.: Prediction of protein-protein interactions based on protein-protein correlation using least squares regression. Curr. Protein Pept. Sci. 15(6), 553–560 (2014)
Vandereyken, K., Leene, J.V., et al.: Hub protein controversy: taking a closer look at plant stress response hub. Front. Plant Sci. 9, 694 (2018)
Mirzarezaee, M., Araabi, B.N., et al.: Features analysis for identification of date and party hubs in protein interaction network of Saccharomyces Cerevisiae. BMC Syst. Biol. 4, 172 (2010)
Yu, H., Greenbaum, D., Xin, L.H., et al.: Genomic analysis of essentiality within protein networks. Trends Genet. 20(6), 227–231 (2004)
He, X., Zhang, J.: Why do hubs tend to be essential in protein networks? PLoS Genet. 2(6), 88–96 (2006)
Yu, H., Kim, P.M., Sprecher, E., et al.: The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Comput. Biol. 3(4), e59 (2007)
Raman, K., Damaraju, N., Joshi, G.K.: The organisational structure of protein networks: revisiting the centrality-lethality hypothesis. Syst. Synth. Biol. 8, 3–81 (2014)
McCormack, M.E., Lopez, J.A., Crocker, T.H., et al.: Making the right connections: network biology and plant immune system dynamics. Curr. Plant Biol. 5, 2–12 (2016)
Han, J.D., Bertin, N., Hao, T., et al.: Evidence for dynamically organized modularity in the yeast protein-protein interaction network. Nature 430, 88–93 (2004)
Yamamoto, N.: Hot spot of structural ambivalence in prion protein revealed by secondary structure principal component analysis. J. Phys. Chem. 118(33), 9826–9833 (2014)
Lin, X.L., Zhang, X.L.: Prediction of hot regions in PPIs based on improved local community structure detecting. IEEE/ACM Trans. Comput. Biol. Bioinform. 99, 1–1(2018)
Engin, G., Attila, G., Ozlem, K.: Analysis of hot region organization in hub protein. Ann. Biomed. Eng. 38(6), 2068–2078 (2010)
Ekman, D., Light, S., Bjorklund, A.K., et al.: What properties characterize the hub proteins of the protein-protein interaction network of Saccharomyces cerevisiae? Genome Biol. 7(6), R45 (2006)
Tuncbag, N.A., Gursoy, E., et al.: Architectures and functional coverage of protein-protein interfaces. J. Mol. Biol. 381(3), 785–802 (2008)
Tuncbag, N., Keskin, O., Gursoy, A.: HotPoint: hot spot prediction server for protein interfaces. Nucleic Acids Res. 38, w402–w406 (2010)
Mustapha, I.B., Saeed, F.: Bioactive molecule prediction using extreme gradient boosting. Molecules 21(8), 983 (2016)
Thorn, K.S., Bogan, A.A.: ASEdb: a data base of alanine mutations and their effects on the free energy of binding in protein interactions. Bioinformatics 17(3), 284–285 (2001)
Moal, I.H., Fernández-Recio, J.: SKEMPI: a structural kinetic and energetic database of mutant protein interactions and its use in empirical models. Bioinformatics 28(20), 2600–2607 (2012)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Acknowledgment
The authors thank the members of Machine Learning and Artificial Intelligence Laboratory, School of Computer Science and Technology, Wuhan University of Science and Technology, for their helpful discussion within seminars. This work was supported in part by Hubei Province Natural Science Foundation of China (No. 2018CFB526), by National Natural Science Foundation of China (No. 61502356).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Lin, X., Zhou, F. (2019). Effective Analysis of Hot Spots in Hub Protein Interfaces Based on Random Forest. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_31
Download citation
DOI: https://doi.org/10.1007/978-3-030-26969-2_31
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26968-5
Online ISBN: 978-3-030-26969-2
eBook Packages: Computer ScienceComputer Science (R0)