Abstract
Protein-protein interactions (PPIs) play major roles in most biological processes. Although a number of high-throughput technologies have been established for generating PPIs, it still has unavoidable problems such as time-consuming and labor intensive. In this paper, we develop a novel computational method for predicting PPIs by combining dual-tree complex wavelet transform (DTCWT) on substitution matrix representation (SMR) and weighted sparse representation-based classifier (WSRC). When predicting PPIs of Yeast and Human datasets, the proposed method obtained remarkable results with average accuracies as high as 97.12% and 97.56%, respectively. The performance of the proposed method is obviously better than the existing methods. Furthermore, we compare it with the superior support vector machine (SVM) classifier for further evaluating the prediction performance of our method. The promising results illustrate that our method is robust and stable for predicting PPIs, and it is anticipated that it would be a useful tool to predict PPIs in a large-scale.
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References
Nishihara, T., Nishikawa, J., Kanayama, T., Dakeyama, F., Saito, K., Imagawa, M., et al.: Estrogenic activities of 517 chemicals by yeast two-hybrid assay. J. Health Sci. 46, 282–298 (2000)
Sato, T., Hanada, M., Bodrug, S., et al.: Interactions among members of the Bcl-2 protein family analyzed with a yeast two-hybrid system. Proc. Natl. Acad. Sci. 91, 9238–9242 (1994)
Puig, O., Caspary, F., Rigaut, G., et al.: The tandem affinity purification (TAP) method: a general procedure of protein complex purification. Methods 24, 218–229 (2001)
Ho, Y., Gruhler, A., Heilbut, A., Bader, G.D., Moore, L., Adams, S.-L., et al.: Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 415, 180–183 (2002)
You, Z.H., Zhou, M.C., Luo, X., Li, S.: Highly efficient framework for predicting interactions between protein. IEEE Trans. Cybernet. 47, 731–743 (2016)
Wang, L., et al.: Using two-dimensional principal component analysis and rotation forest for prediction of protein-protein interactions. Scientific Reports 8, 1–10 (2018)
Wang, L., You, Z.H., Xia, S.X., Liu, F., Chen, X., Yan, X., Zhou, Y.: Advancing the prediction accuracy of protein-protein interactions by utilizing evolutionary information from position-specific scoring matrix and ensemble classifier. J. Theor. Biol. 418, 105–110 (2017)
Li, Z.W., et al.: Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier. Oncotarget 8(14), 23638 (2017)
You, Z.H., Li, X., Chan, K.C.C.: An Improved sequence-based prediction protocol for protein-protein interactions using amino acids substitution matrix and rotation forest ensemble classifiers. Neurocomputing 228, 277–282 (2017)
Wang, L., et al.: An ensemble approach for large-scale identification of protein-protein interactions using the alignments of multiple sequences. Oncotarget 8, 5149 (2017)
Wang, Y.B., et al.: Predicting protein-protein interactions from protein sequences by stacked sparse auto-encoder deep neural network. Molecular BioSyst. 13, 1336–1344 (2017)
An, J.Y., You, Z.H., Chen, X., Huang, D.S., Yan, G.Y.: Robust and accurate prediction of protein self-interactions from amino acids sequence using evolutionary information. Molecular BioSyst. 12, 3702–3710 (2016)
Huang, Y.A., et al.: Construction of reliable protein-protein interaction networks using weighted sparse representation based classifier with pseudo substitution matrix representation. Neurocomputing 218, 131–138 (2016)
An, J.Y., Meng, F.R., You, Z.H., Chen, X.: Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model. Protein Sci. 25, 1825–1833 (2016)
You, Z.H., Chan, K.C.C.: Prediction of protein-protein interactions from primary protein sequence using random forest model with a novel multi-scale local feature representation. PLoS ONE 10, e0131091 (2015)
You, Z.H., Zhu, L., Zheng, C.H., Yu, H.J., Deng, S.P., Ji, Z.: Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set. BMC Bioinform. 15(15), s9 (2014)
Li, J., Shi X., You, Z.H., Yi, H.C., Chen, Z., Lin, Q., et al.: Using weighted extreme learning machine combined with scale-invariant feature transform to predict protein-protein interactions from protein evolutionary information. IEEE/ACM Trans. Comput. Biol. Bioinform. (2020)
An, J.-Y., You, Z.H., Zhou, Y., Wang, D.F.: Sequence-based prediction of protein-protein interactions using gray wolf optimizer-based relevance vector machine. Evol. Bioinform. 15, 1176934319844522 (2019)
Zhu, H.J., You, Z.H., Shi, W.L., Xu, S.K., Jiang, T.H., Zhuang, L.H.: Improved prediction of protein-protein interactions using descriptors derived from PSSM via Gray Level Co-Occurrence Matrix. IEEE Access 7, 49456–49465 (2019)
You, Z.H., Huang, W.Z., Zhang, S., Huang, Y.A., Yu, C.Q., Li, L.P.: An efficient ensemble learning approach for predicting protein-protein interactions by integrating protein primary sequence and evolutionary information. IEEE/ACM Trans. Comput. Biol. Bioinform. 16, 809–817 (2018)
Huang, Y.A., You, Z.H., Chen, X., Chan, K., Luo, X.: Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding. BMC Bioinform. 17, 184 (2016)
Wen, Y.T., Lei, H.J., You, Z.H., Lei, B.Y., Chen, X., Li, L.P.: Prediction of protein-protein interactions by label propagation with protein evolutionary and chemical information derived from heterogeneous network. J. Theor. Biol. 430, 9–20 (2017)
Wong, L., You, Z.-H., Li, S., Huang, Y.-A., Liu, G.: Detection of protein-protein interactions from amino acid sequences using a rotation forest model with a novel PR-LPQ descriptor. In: Huang, D.-S., Han, K. (eds.) ICIC 2015. LNCS (LNAI), vol. 9227, pp. 713–720. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-22053-6_75
Huang, Q.Y., You, Z.H., Li, S., Zhu, Z.X.: Using Chou’s amphiphilic pseudo-amino acid composition and extreme learning machine for prediction of protein-protein interactions. In: IEEE, pp. 2952–2956 (2014)
You, Z.H., Yu, J.Z., Zhu, L., Li, S., Wen, Z.K.: A MapReduce based parallel SVM for large-scale predicting protein–protein interactions. Neurocomputing 145, 37–43 (2014)
Yu, X., Zheng, X., Liu, T., Dou, Y., Wang, J.: Predicting subcellular location of apoptosis proteins with pseudo amino acid composition: approach from amino acid substitution matrix and auto covariance transformation. Amino Acids 42, 1619–1625 (2012)
Selesnick, I.W., Baraniuk, R.G., Kingsbury, N.G.: The dual-tree complex wavelet transform. IEEE Signal Process. Mag. 22, 123–151 (2005)
Kingsbury, N.: Complex wavelets for shift invariant analysis and filtering of signals. Appl. Comput. Harmon. Anal. 10, 234–253 (2001)
Barri, A., Dooms, A., Schelkens, P.: The near shift-invariance of the dual-tree complex wavelet transform revisited. J. Math. Anal. Appl. 389, 1303–1314 (2012)
Yin, J., Liu, Z., Jin, Z., Yang, W.: Kernel sparse representation based classification. Neurocomputing 77, 120–128 (2012)
Gao, Y., Ma, J., Yuille, A.L.: Semi-supervised sparse representation based classification for face recognition with insufficient labeled samples. IEEE Trans. Image Process. 26, 2545–2560 (2017)
Candes, E.J., Tao, T.: Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans. Inf. Theory 52, 5406–5425 (2006)
Chen, S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by basis pursuit. SIAM J. Sci. Comput. 20, 33–61 (1998)
Liu, C.H.W.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Process. 11, 467–476 (2002)
Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., Gong, Y.: Locality-constrained linear coding for image classification. In: IEEE, pp. 3360–3367 (2010)
Lu, C.Y., Min, H., Gui, J., Zhu, L., Lei, Y.K.: Face recognition via weighted sparse representation. J. Vis. Commun. Image Rep. 24, 111–116 (2013)
Zhang, P.: Model selection via multifold cross validation. Ann. Stat. 21, 299–313 (1993)
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This work is supported by the National Science Foundation of China under Grants 61873212.
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Pan, J., You, ZH., Yu, CQ., Li, LP., Zhan, Xk. (2020). Predicting Protein-Protein Interactions from Protein Sequence Information Using Dual-Tree Complex Wavelet Transform. In: Huang, DS., Jo, KH. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12464. Springer, Cham. https://doi.org/10.1007/978-3-030-60802-6_13
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