Nglyc: A Random Forest Method for Prediction of N-Glycosylation Sites in Eukaryotic Protein Sequence.

Pugalenthi, Ganesan; Nithya, Varadharaju; Chou, Kuo-Chen; Archunan, Govindaraju

Pugalenthi, Ganesan; Nithya, Varadharaju; Chou, Kuo-Chen; Archunan, Govindaraju.

Affiliation

Pugalenthi G; Pheromone Technology Laboratory, Department of Animal Science, Bharathidasan University, Tiruchirappalli- 620024, India.
Nithya V; Department of Animal Health Management, Alagappa University, Karaikudi-630003, India.
Chou KC; Gordon Life Science Institute, San Diego, CA 92130, United States.
Archunan G; Pheromone Technology Laboratory, Department of Animal Science, Bharathidasan University, Tiruchirappalli- 620024, India.

Protein Pept Lett ; 27(3): 178-186, 2020.

Article in En | MEDLINE | ID: mdl-31577193

Subject(s)

Computational Biology/methods; Proteins/chemistry; Sequence Analysis, Protein/methods; Animals; Databases, Protein; Glycosylation; Humans; Mice; Software

Key words

N-glycosylation; glycoproteins; glycosites; machine learning method; protein function; protein sequence.

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Proteins / Computational Biology / Sequence Analysis, Protein Type of study: Clinical_trials / Prognostic_studies / Risk_factors_studies Limits: Animals / Humans Language: En Journal: Protein Pept Lett Journal subject: BIOQUIMICA Year: 2020 Document type: Article Affiliation country: India Country of publication: Netherlands

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