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 proceedingChapter

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 languageEnglish
Title of host publicationLecture Notes in Networks and Systems
PublisherSpringer India
Pages324-332
Number of pages9
DOIs
Publication statusPublished - Jan 1 2019
Externally publishedYes

Publication series

NameLecture Notes in Networks and Systems
Volume63
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

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  • Cite this

    Nguyen, V. N., Do, H. K., Tran, T. X., Le, N. Q. K., Le, A. T., & Lee, T. Y. (2019). Exploiting two-layer support vector machine to predict protein sumoylation sites. In Lecture Notes in Networks and Systems (pp. 324-332). (Lecture Notes in Networks and Systems; Vol. 63). Springer India. https://doi.org/10.1007/978-3-030-04792-4_43