Prediction of protein-protein interactions between HIV-1 and human using support vector machine combined with multivariate mutual information

Mohamad Irlin Sunggawa; Alhadi Bustamam; Devvi Sarwinda; Patuan Pangihutan Tampubolon; Wibowo Mangunwardoyo

doi:10.1109/IBIOMED50285.2020.9487598

Prediction of protein-protein interactions between HIV-1 and human using support vector machine combined with multivariate mutual information

Mohamad Irlin Sunggawa, Alhadi Bustamam, Devvi Sarwinda, Patuan Pangihutan Tampubolon, Wibowo Mangunwardoyo

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

1 Citation (Scopus)

Abstract

Protein-protein interactions are the center of many biological processes. Studying protein-protein interaction have a vital role in learning how disease occurs and also crucial in drug design. Studying protein-protein interaction consumes much time and is very costly; even then the results are not always positive. Researchers have developed numerous methods to predict is there any interaction between a pair of protein using only use their amino acid sequences. In this paper, we use support vector machine (SVM) as the classifier combined with four types of multivariate mutual information (MMI) as the descriptor of amino acid sequences to predict protein-protein interaction (PPIs) based on interaction data between HIV1 and Humans. We use data of protein interactions between HIV1 and Humans that are provided in NCBI. The results show that MM1 type I, II, III, and IV have an average accuracy of 84.55%, 84.90%, 83.56%, and 83.61% respectively, when tested on validation datasets.

Original language	English
Title of host publication	IBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	77-81
Number of pages	5
ISBN (Electronic)	9781728171562
DOIs	https://doi.org/10.1109/IBIOMED50285.2020.9487598
Publication status	Published - 6 Oct 2020
Event	37th International Conference on Biomedical Engineering, IBIOMED 2020 - Yogyakarta, Indonesia Duration: 6 Oct 2020 → 8 Oct 2020

Publication series

Name	IBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering

Conference

Conference	37th International Conference on Biomedical Engineering, IBIOMED 2020
Country/Territory	Indonesia
City	Yogyakarta
Period	6/10/20 → 8/10/20

Keywords

Multivariate Mutual Information
Protein-protein Interactions
Support Vector Machine

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/IBIOMED50285.2020.9487598

Cite this

Sunggawa, M. I., Bustamam, A., Sarwinda, D., Tampubolon, P. P., & Mangunwardoyo, W. (2020). Prediction of protein-protein interactions between HIV-1 and human using support vector machine combined with multivariate mutual information. In IBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering (pp. 77-81). Article 9487598 (IBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IBIOMED50285.2020.9487598

Sunggawa, Mohamad Irlin ; Bustamam, Alhadi ; Sarwinda, Devvi et al. / Prediction of protein-protein interactions between HIV-1 and human using support vector machine combined with multivariate mutual information. IBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 77-81 (IBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering).

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title = "Prediction of protein-protein interactions between HIV-1 and human using support vector machine combined with multivariate mutual information",

abstract = "Protein-protein interactions are the center of many biological processes. Studying protein-protein interaction have a vital role in learning how disease occurs and also crucial in drug design. Studying protein-protein interaction consumes much time and is very costly; even then the results are not always positive. Researchers have developed numerous methods to predict is there any interaction between a pair of protein using only use their amino acid sequences. In this paper, we use support vector machine (SVM) as the classifier combined with four types of multivariate mutual information (MMI) as the descriptor of amino acid sequences to predict protein-protein interaction (PPIs) based on interaction data between HIV1 and Humans. We use data of protein interactions between HIV1 and Humans that are provided in NCBI. The results show that MM1 type I, II, III, and IV have an average accuracy of 84.55%, 84.90%, 83.56%, and 83.61% respectively, when tested on validation datasets.",

keywords = "Multivariate Mutual Information, Protein-protein Interactions, Support Vector Machine",

author = "Sunggawa, {Mohamad Irlin} and Alhadi Bustamam and Devvi Sarwinda and Tampubolon, {Patuan Pangihutan} and Wibowo Mangunwardoyo",

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Sunggawa, MI, Bustamam, A, Sarwinda, D, Tampubolon, PP & Mangunwardoyo, W 2020, Prediction of protein-protein interactions between HIV-1 and human using support vector machine combined with multivariate mutual information. in IBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering., 9487598, IBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering, Institute of Electrical and Electronics Engineers Inc., pp. 77-81, 37th International Conference on Biomedical Engineering, IBIOMED 2020, Yogyakarta, Indonesia, 6/10/20. https://doi.org/10.1109/IBIOMED50285.2020.9487598

Prediction of protein-protein interactions between HIV-1 and human using support vector machine combined with multivariate mutual information. / Sunggawa, Mohamad Irlin; Bustamam, Alhadi; Sarwinda, Devvi et al.
IBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering. Institute of Electrical and Electronics Engineers Inc., 2020. p. 77-81 9487598 (IBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering).

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

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T1 - Prediction of protein-protein interactions between HIV-1 and human using support vector machine combined with multivariate mutual information

AU - Sunggawa, Mohamad Irlin

AU - Bustamam, Alhadi

AU - Sarwinda, Devvi

AU - Tampubolon, Patuan Pangihutan

AU - Mangunwardoyo, Wibowo

PY - 2020/10/6

Y1 - 2020/10/6

N2 - Protein-protein interactions are the center of many biological processes. Studying protein-protein interaction have a vital role in learning how disease occurs and also crucial in drug design. Studying protein-protein interaction consumes much time and is very costly; even then the results are not always positive. Researchers have developed numerous methods to predict is there any interaction between a pair of protein using only use their amino acid sequences. In this paper, we use support vector machine (SVM) as the classifier combined with four types of multivariate mutual information (MMI) as the descriptor of amino acid sequences to predict protein-protein interaction (PPIs) based on interaction data between HIV1 and Humans. We use data of protein interactions between HIV1 and Humans that are provided in NCBI. The results show that MM1 type I, II, III, and IV have an average accuracy of 84.55%, 84.90%, 83.56%, and 83.61% respectively, when tested on validation datasets.

AB - Protein-protein interactions are the center of many biological processes. Studying protein-protein interaction have a vital role in learning how disease occurs and also crucial in drug design. Studying protein-protein interaction consumes much time and is very costly; even then the results are not always positive. Researchers have developed numerous methods to predict is there any interaction between a pair of protein using only use their amino acid sequences. In this paper, we use support vector machine (SVM) as the classifier combined with four types of multivariate mutual information (MMI) as the descriptor of amino acid sequences to predict protein-protein interaction (PPIs) based on interaction data between HIV1 and Humans. We use data of protein interactions between HIV1 and Humans that are provided in NCBI. The results show that MM1 type I, II, III, and IV have an average accuracy of 84.55%, 84.90%, 83.56%, and 83.61% respectively, when tested on validation datasets.

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M3 - Conference contribution

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PB - Institute of Electrical and Electronics Engineers Inc.

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ER -

Sunggawa MI, Bustamam A, Sarwinda D, Tampubolon PP, Mangunwardoyo W. Prediction of protein-protein interactions between HIV-1 and human using support vector machine combined with multivariate mutual information. In IBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering. Institute of Electrical and Electronics Engineers Inc. 2020. p. 77-81. 9487598. (IBIOMED 2020 - Proceedings of the 37th International Conference on Biomedical Engineering). doi: 10.1109/IBIOMED50285.2020.9487598

Prediction of protein-protein interactions between HIV-1 and human using support vector machine combined with multivariate mutual information

Abstract

Publication series

Conference

Keywords

UN SDGs

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