Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites.
feature descriptor
lysine succinylation
machine learning
sequence analysis
tool development
Journal
Cells
ISSN: 2073-4409
Titre abrégé: Cells
Pays: Switzerland
ID NLM: 101600052
Informations de publication
Date de publication:
28 01 2019
28 01 2019
Historique:
received:
26
12
2018
revised:
24
01
2019
accepted:
24
01
2019
entrez:
31
1
2019
pubmed:
31
1
2019
medline:
31
1
2019
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 30696115
pii: cells8020095
doi: 10.3390/cells8020095
pmc: PMC6406724
pii:
doi:
Substances chimiques
Succinic Acid
AB6MNQ6J6L
Lysine
K3Z4F929H6
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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