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Cells 2019, 8(2), 95; https://doi.org/10.3390/cells8020095

Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites

1
Department of Bioscience and Bioinformatics, Kyushu Institute of Technology, 680–4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
2
Biomedical Informatics R&D Center, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka, Fukuoka 820-8502, Japan
*
Author to whom correspondence should be addressed.
Received: 26 December 2018 / Revised: 24 January 2019 / Accepted: 24 January 2019 / Published: 28 January 2019
(This article belongs to the Special Issue Bioinformatics and Computational Biology 2019)
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Abstract

Lysine succinylation is a form of posttranslational modification of the proteins that play an essential functional role in every aspect of cell metabolism in both prokaryotes and eukaryotes. Aside from experimental identification of succinylation sites, there has been an intense effort geared towards the development of sequence-based prediction through machine learning, due to its promising and essential properties of being highly accurate, robust and cost-effective. In spite of these advantages, there are several problems that are in need of attention in the design and development of succinylation site predictors. Notwithstanding of many studies on the employment of machine learning approaches, few articles have examined this bioinformatics field in a systematic manner. Thus, we review the advancements regarding the current state-of-the-art prediction models, datasets, and online resources and illustrate the challenges and limitations to present a useful guideline for developing powerful succinylation site prediction tools. View Full-Text
Keywords: lysine succinylation; sequence analysis; machine learning; tool development; feature descriptor lysine succinylation; sequence analysis; machine learning; tool development; feature descriptor
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Hasan, M.M.; Khatun, M.S.; Kurata, H. Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites. Cells 2019, 8, 95.

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