Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-interacting Proteins

Yan Bin Wang; Zhu Hong You; Hai cheng Yi; Zhan Heng Chen; Zhen Hao Guo; Kai Zheng

doi:10.1007/978-3-030-26969-2_44

Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-interacting Proteins

Yan Bin Wang, Zhu Hong You, Hai cheng Yi, Zhan Heng Chen, Zhen Hao Guo, Kai Zheng

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

1 Scopus citations

Abstract

Self-interacting proteins (SIPs) play a crucial role in investigation of various biochemical developments. In this work, a novel computational method was proposed for accelerating SIPs validation only using protein sequence. Firstly, the protein sequence was represented as Position-Specific Weight Matrix (PSWM) containing protein evolutionary information. Then, we incorporated the Legendre Moment (LM) and Sparse Principal Component Analysis (SPCA) to extract essential and anti-noise evolutionary feature from the PSWM. Finally, we utilized robust Probabilistic Classification Vector Machine (PCVM) classifier to carry out prediction. In the cross-validated experiment, the proposed method exhibits high accuracy performance with 95.54% accuracy on S.erevisiae dataset, which is a significant improvement compared to several competing SIPs predictors. The empirical test reveal that the proposed method can efficiently extracts salient features from protein sequences and accurately predict potential SIPs.

Original language	English
Title of host publication	Intelligent Computing Theories and Application - 15th International Conference, ICIC 2019, Proceedings
Editors	De-Shuang Huang, Kang-Hyun Jo, Zhi-Kai Huang
Publisher	Springer Verlag
Pages	460-467
Number of pages	8
ISBN (Print)	9783030269685
DOIs	https://doi.org/10.1007/978-3-030-26969-2_44
State	Published - 2019
Externally published	Yes
Event	15th International Conference on Intelligent Computing, ICIC 2019 - Nanchang, China Duration: 3 Aug 2019 → 6 Aug 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11644 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Intelligent Computing, ICIC 2019
Country/Territory	China
City	Nanchang
Period	3/08/19 → 6/08/19

Keywords

Legendre Moment
Probabilistic Classification Vector Machine
Protein-protein interactions
Self-interacting proteins

Access to Document

10.1007/978-3-030-26969-2_44

Cite this

Wang, Y. B., You, Z. H., Yi, H. C., Chen, Z. H., Guo, Z. H., & Zheng, K. (2019). Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-interacting Proteins. In D.-S. Huang, K.-H. Jo, & Z.-K. Huang (Eds.), Intelligent Computing Theories and Application - 15th International Conference, ICIC 2019, Proceedings (pp. 460-467). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11644 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-26969-2_44

Wang, Yan Bin ; You, Zhu Hong ; Yi, Hai cheng et al. / Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-interacting Proteins. Intelligent Computing Theories and Application - 15th International Conference, ICIC 2019, Proceedings. editor / De-Shuang Huang ; Kang-Hyun Jo ; Zhi-Kai Huang. Springer Verlag, 2019. pp. 460-467 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{89623bb4079643089c4ec297342f32af,

title = "Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-interacting Proteins",

abstract = "Self-interacting proteins (SIPs) play a crucial role in investigation of various biochemical developments. In this work, a novel computational method was proposed for accelerating SIPs validation only using protein sequence. Firstly, the protein sequence was represented as Position-Specific Weight Matrix (PSWM) containing protein evolutionary information. Then, we incorporated the Legendre Moment (LM) and Sparse Principal Component Analysis (SPCA) to extract essential and anti-noise evolutionary feature from the PSWM. Finally, we utilized robust Probabilistic Classification Vector Machine (PCVM) classifier to carry out prediction. In the cross-validated experiment, the proposed method exhibits high accuracy performance with 95.54% accuracy on S.erevisiae dataset, which is a significant improvement compared to several competing SIPs predictors. The empirical test reveal that the proposed method can efficiently extracts salient features from protein sequences and accurately predict potential SIPs.",

keywords = "Legendre Moment, Probabilistic Classification Vector Machine, Protein-protein interactions, Self-interacting proteins",

author = "Wang, {Yan Bin} and You, {Zhu Hong} and Yi, {Hai cheng} and Chen, {Zhan Heng} and Guo, {Zhen Hao} and Kai Zheng",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 15th International Conference on Intelligent Computing, ICIC 2019 ; Conference date: 03-08-2019 Through 06-08-2019",

year = "2019",

doi = "10.1007/978-3-030-26969-2_44",

language = "英语",

isbn = "9783030269685",

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

publisher = "Springer Verlag",

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editor = "De-Shuang Huang and Kang-Hyun Jo and Zhi-Kai Huang",

booktitle = "Intelligent Computing Theories and Application - 15th International Conference, ICIC 2019, Proceedings",

}

Wang, YB, You, ZH , Yi, HC, Chen, ZH, Guo, ZH & Zheng, K 2019, Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-interacting Proteins. in D-S Huang, K-H Jo & Z-K Huang (eds), Intelligent Computing Theories and Application - 15th International Conference, ICIC 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11644 LNCS, Springer Verlag, pp. 460-467, 15th International Conference on Intelligent Computing, ICIC 2019, Nanchang, China, 3/08/19. https://doi.org/10.1007/978-3-030-26969-2_44

Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-interacting Proteins. / Wang, Yan Bin; You, Zhu Hong ; Yi, Hai cheng et al.
Intelligent Computing Theories and Application - 15th International Conference, ICIC 2019, Proceedings. ed. / De-Shuang Huang; Kang-Hyun Jo; Zhi-Kai Huang. Springer Verlag, 2019. p. 460-467 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11644 LNCS).

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

TY - GEN

T1 - Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-interacting Proteins

AU - Wang, Yan Bin

AU - You, Zhu Hong

AU - Yi, Hai cheng

AU - Chen, Zhan Heng

AU - Guo, Zhen Hao

AU - Zheng, Kai

PY - 2019

Y1 - 2019

N2 - Self-interacting proteins (SIPs) play a crucial role in investigation of various biochemical developments. In this work, a novel computational method was proposed for accelerating SIPs validation only using protein sequence. Firstly, the protein sequence was represented as Position-Specific Weight Matrix (PSWM) containing protein evolutionary information. Then, we incorporated the Legendre Moment (LM) and Sparse Principal Component Analysis (SPCA) to extract essential and anti-noise evolutionary feature from the PSWM. Finally, we utilized robust Probabilistic Classification Vector Machine (PCVM) classifier to carry out prediction. In the cross-validated experiment, the proposed method exhibits high accuracy performance with 95.54% accuracy on S.erevisiae dataset, which is a significant improvement compared to several competing SIPs predictors. The empirical test reveal that the proposed method can efficiently extracts salient features from protein sequences and accurately predict potential SIPs.

AB - Self-interacting proteins (SIPs) play a crucial role in investigation of various biochemical developments. In this work, a novel computational method was proposed for accelerating SIPs validation only using protein sequence. Firstly, the protein sequence was represented as Position-Specific Weight Matrix (PSWM) containing protein evolutionary information. Then, we incorporated the Legendre Moment (LM) and Sparse Principal Component Analysis (SPCA) to extract essential and anti-noise evolutionary feature from the PSWM. Finally, we utilized robust Probabilistic Classification Vector Machine (PCVM) classifier to carry out prediction. In the cross-validated experiment, the proposed method exhibits high accuracy performance with 95.54% accuracy on S.erevisiae dataset, which is a significant improvement compared to several competing SIPs predictors. The empirical test reveal that the proposed method can efficiently extracts salient features from protein sequences and accurately predict potential SIPs.

KW - Legendre Moment

KW - Probabilistic Classification Vector Machine

KW - Protein-protein interactions

KW - Self-interacting proteins

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DO - 10.1007/978-3-030-26969-2_44

M3 - 会议稿件

AN - SCOPUS:85070552460

SN - 9783030269685

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

SP - 460

EP - 467

BT - Intelligent Computing Theories and Application - 15th International Conference, ICIC 2019, Proceedings

A2 - Huang, De-Shuang

A2 - Jo, Kang-Hyun

A2 - Huang, Zhi-Kai

PB - Springer Verlag

T2 - 15th International Conference on Intelligent Computing, ICIC 2019

Y2 - 3 August 2019 through 6 August 2019

ER -

Wang YB, You ZH , Yi HC, Chen ZH, Guo ZH, Zheng K. Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-interacting Proteins. In Huang DS, Jo KH, Huang ZK, editors, Intelligent Computing Theories and Application - 15th International Conference, ICIC 2019, Proceedings. Springer Verlag. 2019. p. 460-467. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-26969-2_44

Combining Evolutionary Information and Sparse Bayesian Probability Model to Accurately Predict Self-interacting Proteins

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