Exploiting two-layer support vector machine to predict protein sumoylation sites

Van Nui Nguyen; Huy Khoi Do; Thi Xuan Tran; Nguyen Quoc Khanh Le; Anh Tu Le; Tzong Yi Lee

doi:10.1007/978-3-030-04792-4_43

Exploiting two-layer support vector machine to predict protein sumoylation sites

Van Nui Nguyen, Huy Khoi Do, Thi Xuan Tran, Nguyen Quoc Khanh Le, Anh Tu Le, Tzong Yi Lee

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

In Eukaryotes species, SUMOylation is one of the most important post-translational modification playing significant roles in biological processes and cellular functions. The mechanism caused by SUMOylation process will affect many biological processes, then turn into the changes of a variety of common serious diseases, such as: breast cancer, cardiac, Parkinson’s and Alzheimer’s disease. Due to the very important roles underlying SUMOylation process, the requirement to have extensive knowledge on SUMOylation and its mechanism is emerging as one of the hottest issues. In this study, we will introduce an approach that exploits two-layer support vector machine to identify protein SUMOylation sites based on substrate motifs.

Original language	English
Title of host publication	Lecture Notes in Networks and Systems
Publisher	Springer India
Pages	324-332
Number of pages	9
DOIs	https://doi.org/10.1007/978-3-030-04792-4_43
Publication status	Published - Jan 1 2019
Externally published	Yes

Publication series

Name	Lecture Notes in Networks and Systems
Volume	63
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Keywords

Maximal dependence decomposition
Substrate motif
SUMOylation
Support vector machine (SVM)
Two-layer support vector machine

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

UN SDGs

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

Access to Document

10.1007/978-3-030-04792-4_43

Cite this

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title = "Exploiting two-layer support vector machine to predict protein sumoylation sites",

abstract = "In Eukaryotes species, SUMOylation is one of the most important post-translational modification playing significant roles in biological processes and cellular functions. The mechanism caused by SUMOylation process will affect many biological processes, then turn into the changes of a variety of common serious diseases, such as: breast cancer, cardiac, Parkinson{\textquoteright}s and Alzheimer{\textquoteright}s disease. Due to the very important roles underlying SUMOylation process, the requirement to have extensive knowledge on SUMOylation and its mechanism is emerging as one of the hottest issues. In this study, we will introduce an approach that exploits two-layer support vector machine to identify protein SUMOylation sites based on substrate motifs.",

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AU - Nguyen, Van Nui

AU - Do, Huy Khoi

AU - Tran, Thi Xuan

AU - Le, Nguyen Quoc Khanh

AU - Le, Anh Tu

AU - Lee, Tzong Yi

PY - 2019/1/1

Y1 - 2019/1/1

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AB - In Eukaryotes species, SUMOylation is one of the most important post-translational modification playing significant roles in biological processes and cellular functions. The mechanism caused by SUMOylation process will affect many biological processes, then turn into the changes of a variety of common serious diseases, such as: breast cancer, cardiac, Parkinson’s and Alzheimer’s disease. Due to the very important roles underlying SUMOylation process, the requirement to have extensive knowledge on SUMOylation and its mechanism is emerging as one of the hottest issues. In this study, we will introduce an approach that exploits two-layer support vector machine to identify protein SUMOylation sites based on substrate motifs.

KW - Maximal dependence decomposition

KW - Substrate motif

KW - SUMOylation

KW - Support vector machine (SVM)

KW - Two-layer support vector machine

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Exploiting two-layer support vector machine to predict protein sumoylation sites

Abstract

Publication series

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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