Predicting the state of cysteines based on sequence information

DOI10.1016/J.JTBI.2010.09.002zbMath1410.92085OpenAlexW2094840358WikidataQ42913448 ScholiaQ42913448MaRDI QIDQ1733013

Xianqiang Yang

Publication date: 26 March 2019

Published in: Journal of Theoretical Biology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jtbi.2010.09.002

zbMATH Keywords

support vector machine\(F\)-score functionannotation informationevolution information

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Protein sequences, DNA sequences (92D20)

Uses Software

• LIBLINEAR
• Nuc-ploc
• Euk-PLoc

Cites Work

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• Predicting the disulfide bonding state of cysteines with combinations of kernel machines
• Using the augmented Chou's pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach
• Cooperativity of the oxidization of cysteines in globular proteins

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