Optimization of Feature Weighting for Epitope Classification in B-Cell and SARS Using TVIWACRI-PSO-ELM

Imam Cholissodin; Nanik Suciati; Darlis Herumurti; Riyanarto Sarno; Valentina Yurina

doi:10.1109/ICTS58770.2023.10330844

Optimization of Feature Weighting for Epitope Classification in B-Cell and SARS Using TVIWACRI-PSO-ELM

Imam Cholissodin^*, Nanik Suciati, Darlis Herumurti, Riyanarto Sarno, Valentina Yurina

^*Corresponding author for this work

Brawijaya University

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

1 Citation (Scopus)

Abstract

In the bio-molecular field, epitope classification is essential in vaccine development. A machine learning-based approach has been used for epitope classification using peptide data from b-cell, Severe Acute Respiratory Syndrome (SARS) to predict material for SARS-CoV-2 vaccine. This study aims to improve the performance of epitope classification by weighting peptide features and including input weight and biases of Classifier model using the Time Variant Inertia Weight, Acceleration Coefficients and Random Injection-Particle Swarm optimization (TVIWACRI-PSO) and the Extreme Learning Machine (ELM) classification algorithm. Experiment on b-cell, SARS dataset shows that feature weighting (FW) using TVIWACRI-PSO can improve accuracy performance by 8.4%.

Original language	English
Title of host publication	2023 14th International Conference on Information and Communication Technology and System, ICTS 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	153-158
Number of pages	6
ISBN (Electronic)	9798350312164
DOIs	https://doi.org/10.1109/ICTS58770.2023.10330844
Publication status	Published - 2023
Event	14th International Conference on Information and Communication Technology and System, ICTS 2023 - Surabaya, Indonesia Duration: 4 Oct 2023 → 5 Oct 2023

Publication series

Name	2023 14th International Conference on Information and Communication Technology and System, ICTS 2023

Conference

Conference	14th International Conference on Information and Communication Technology and System, ICTS 2023
Country/Territory	Indonesia
City	Surabaya
Period	4/10/23 → 5/10/23

Keywords

epitope b-cell and sars
feature weight
sars-cov-2 vaccine
tviwacri-pso-elm

UN SDGs

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

Access to Document

10.1109/ICTS58770.2023.10330844

Cite this

Cholissodin, I., Suciati, N., Herumurti, D., Sarno, R., & Yurina, V. (2023). Optimization of Feature Weighting for Epitope Classification in B-Cell and SARS Using TVIWACRI-PSO-ELM. In 2023 14th International Conference on Information and Communication Technology and System, ICTS 2023 (pp. 153-158). (2023 14th International Conference on Information and Communication Technology and System, ICTS 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICTS58770.2023.10330844

Cholissodin, Imam ; Suciati, Nanik ; Herumurti, Darlis et al. / Optimization of Feature Weighting for Epitope Classification in B-Cell and SARS Using TVIWACRI-PSO-ELM. 2023 14th International Conference on Information and Communication Technology and System, ICTS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 153-158 (2023 14th International Conference on Information and Communication Technology and System, ICTS 2023).

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title = "Optimization of Feature Weighting for Epitope Classification in B-Cell and SARS Using TVIWACRI-PSO-ELM",

abstract = "In the bio-molecular field, epitope classification is essential in vaccine development. A machine learning-based approach has been used for epitope classification using peptide data from b-cell, Severe Acute Respiratory Syndrome (SARS) to predict material for SARS-CoV-2 vaccine. This study aims to improve the performance of epitope classification by weighting peptide features and including input weight and biases of Classifier model using the Time Variant Inertia Weight, Acceleration Coefficients and Random Injection-Particle Swarm optimization (TVIWACRI-PSO) and the Extreme Learning Machine (ELM) classification algorithm. Experiment on b-cell, SARS dataset shows that feature weighting (FW) using TVIWACRI-PSO can improve accuracy performance by 8.4%.",

keywords = "epitope b-cell and sars, feature weight, sars-cov-2 vaccine, tviwacri-pso-elm",

author = "Imam Cholissodin and Nanik Suciati and Darlis Herumurti and Riyanarto Sarno and Valentina Yurina",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 14th International Conference on Information and Communication Technology and System, ICTS 2023 ; Conference date: 04-10-2023 Through 05-10-2023",

year = "2023",

doi = "10.1109/ICTS58770.2023.10330844",

language = "English",

series = "2023 14th International Conference on Information and Communication Technology and System, ICTS 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Cholissodin, I, Suciati, N , Herumurti, D , Sarno, R & Yurina, V 2023, Optimization of Feature Weighting for Epitope Classification in B-Cell and SARS Using TVIWACRI-PSO-ELM. in 2023 14th International Conference on Information and Communication Technology and System, ICTS 2023. 2023 14th International Conference on Information and Communication Technology and System, ICTS 2023, Institute of Electrical and Electronics Engineers Inc., pp. 153-158, 14th International Conference on Information and Communication Technology and System, ICTS 2023, Surabaya, Indonesia, 4/10/23. https://doi.org/10.1109/ICTS58770.2023.10330844

Optimization of Feature Weighting for Epitope Classification in B-Cell and SARS Using TVIWACRI-PSO-ELM. / Cholissodin, Imam; Suciati, Nanik ; Herumurti, Darlis et al.
2023 14th International Conference on Information and Communication Technology and System, ICTS 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 153-158 (2023 14th International Conference on Information and Communication Technology and System, ICTS 2023).

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

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AU - Herumurti, Darlis

AU - Sarno, Riyanarto

AU - Yurina, Valentina

PY - 2023

Y1 - 2023

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AB - In the bio-molecular field, epitope classification is essential in vaccine development. A machine learning-based approach has been used for epitope classification using peptide data from b-cell, Severe Acute Respiratory Syndrome (SARS) to predict material for SARS-CoV-2 vaccine. This study aims to improve the performance of epitope classification by weighting peptide features and including input weight and biases of Classifier model using the Time Variant Inertia Weight, Acceleration Coefficients and Random Injection-Particle Swarm optimization (TVIWACRI-PSO) and the Extreme Learning Machine (ELM) classification algorithm. Experiment on b-cell, SARS dataset shows that feature weighting (FW) using TVIWACRI-PSO can improve accuracy performance by 8.4%.

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ER -

Cholissodin I, Suciati N , Herumurti D , Sarno R, Yurina V. Optimization of Feature Weighting for Epitope Classification in B-Cell and SARS Using TVIWACRI-PSO-ELM. In 2023 14th International Conference on Information and Communication Technology and System, ICTS 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 153-158. (2023 14th International Conference on Information and Communication Technology and System, ICTS 2023). doi: 10.1109/ICTS58770.2023.10330844

Optimization of Feature Weighting for Epitope Classification in B-Cell and SARS Using TVIWACRI-PSO-ELM

Abstract

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Keywords

UN SDGs

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