Predicting Protein-Protein Interactions from Protein Sequence Information Using Dual-Tree Complex Wavelet Transform

Jie Pan; Zhu Hong You; Chang Qing Yu; Li Ping Li; Xin ke Zhan

doi:10.1007/978-3-030-60802-6_13

Predicting Protein-Protein Interactions from Protein Sequence Information Using Dual-Tree Complex Wavelet Transform

Jie Pan, Zhu Hong You, Chang Qing Yu, Li Ping Li, Xin ke Zhan

Xijing University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English
Title of host publication	Intelligent Computing Theories and Application - 16th International Conference, ICIC 2020, Proceedings
Editors	De-Shuang Huang, Kang-Hyun Jo
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	132-142
Number of pages	11
ISBN (Print)	9783030608019
DOIs	https://doi.org/10.1007/978-3-030-60802-6_13
State	Published - 2020
Externally published	Yes
Event	16th International Conference on Intelligent Computing, ICIC 2020 - Bari , Italy Duration: 2 Oct 2020 → 5 Oct 2020

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12464 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Intelligent Computing, ICIC 2020
Country/Territory	Italy
City	Bari
Period	2/10/20 → 5/10/20

Keywords

Dual-tree complex wavelet transform
Protein sequence
Protein-protein interactions
Weighted sparse representation

Access to Document

10.1007/978-3-030-60802-6_13

Cite this

Pan, J., You, Z. H., Yu, C. Q., Li, L. P., & Zhan, X. K. (2020). Predicting Protein-Protein Interactions from Protein Sequence Information Using Dual-Tree Complex Wavelet Transform. In D.-S. Huang, & K.-H. Jo (Eds.), Intelligent Computing Theories and Application - 16th International Conference, ICIC 2020, Proceedings (pp. 132-142). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12464 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-60802-6_13

Pan, Jie ; You, Zhu Hong ; Yu, Chang Qing et al. / Predicting Protein-Protein Interactions from Protein Sequence Information Using Dual-Tree Complex Wavelet Transform. Intelligent Computing Theories and Application - 16th International Conference, ICIC 2020, Proceedings. editor / De-Shuang Huang ; Kang-Hyun Jo. Springer Science and Business Media Deutschland GmbH, 2020. pp. 132-142 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{0573503a4e8f42089e65c8f0ba293588,

title = "Predicting Protein-Protein Interactions from Protein Sequence Information Using Dual-Tree Complex Wavelet Transform",

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.",

keywords = "Dual-tree complex wavelet transform, Protein sequence, Protein-protein interactions, Weighted sparse representation",

author = "Jie Pan and You, {Zhu Hong} and Yu, {Chang Qing} and Li, {Li Ping} and Zhan, {Xin ke}",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 16th International Conference on Intelligent Computing, ICIC 2020 ; Conference date: 02-10-2020 Through 05-10-2020",

year = "2020",

doi = "10.1007/978-3-030-60802-6_13",

language = "英语",

isbn = "9783030608019",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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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 D-S Huang & K-H Jo (eds), Intelligent Computing Theories and Application - 16th International Conference, ICIC 2020, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12464 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 132-142, 16th International Conference on Intelligent Computing, ICIC 2020, Bari , Italy, 2/10/20. https://doi.org/10.1007/978-3-030-60802-6_13

Predicting Protein-Protein Interactions from Protein Sequence Information Using Dual-Tree Complex Wavelet Transform. / Pan, Jie; You, Zhu Hong; Yu, Chang Qing et al.
Intelligent Computing Theories and Application - 16th International Conference, ICIC 2020, Proceedings. ed. / De-Shuang Huang; Kang-Hyun Jo. Springer Science and Business Media Deutschland GmbH, 2020. p. 132-142 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12464 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Predicting Protein-Protein Interactions from Protein Sequence Information Using Dual-Tree Complex Wavelet Transform

AU - Pan, Jie

AU - You, Zhu Hong

AU - Yu, Chang Qing

AU - Li, Li Ping

AU - Zhan, Xin ke

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Dual-tree complex wavelet transform

KW - Protein sequence

KW - Protein-protein interactions

KW - Weighted sparse representation

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U2 - 10.1007/978-3-030-60802-6_13

DO - 10.1007/978-3-030-60802-6_13

M3 - 会议稿件

AN - SCOPUS:85094110342

SN - 9783030608019

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 132

EP - 142

BT - Intelligent Computing Theories and Application - 16th International Conference, ICIC 2020, Proceedings

A2 - Huang, De-Shuang

A2 - Jo, Kang-Hyun

PB - Springer Science and Business Media Deutschland GmbH

T2 - 16th International Conference on Intelligent Computing, ICIC 2020

Y2 - 2 October 2020 through 5 October 2020

ER -

Pan J, You ZH, Yu CQ, Li LP, Zhan XK. Predicting Protein-Protein Interactions from Protein Sequence Information Using Dual-Tree Complex Wavelet Transform. In Huang DS, Jo KH, editors, Intelligent Computing Theories and Application - 16th International Conference, ICIC 2020, Proceedings. Springer Science and Business Media Deutschland GmbH. 2020. p. 132-142. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-60802-6_13

Predicting Protein-Protein Interactions from Protein Sequence Information Using Dual-Tree Complex Wavelet Transform

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