Predicting DNA- and RNA-binding proteins from sequences with kernel methods
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Publication:1620818
DOI10.1016/j.jtbi.2009.01.024zbMath1402.92332OpenAlexW2104813168WikidataQ44962243 ScholiaQ44962243MaRDI QIDQ1620818
Xiaojian Shao, Yong Wang, Ling Jing, Ling-Yun Wu, Nai-Yang Deng, Ying-Jie Tian
Publication date: 14 November 2018
Published in: Journal of Theoretical Biology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jtbi.2009.01.024
Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Protein sequences, DNA sequences (92D20)
Related Items (5)
Annotating the protein-RNA interaction sites in proteins using evolutionary information and protein backbone structure ⋮ Comprehensive comparative analysis and identification of RNA-binding protein domains: multi-class classification and feature selection ⋮ Predicting DNA binding proteins using support vector machine with hybrid fractal features ⋮ Prediction of posttranslational modification sites from amino acid sequences with kernel methods ⋮ Some remarks on protein attribute prediction and pseudo amino acid composition
Uses Software
Cites Work
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- The modified Mahalanobis discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition
- Predicting protein structural class based on multi-features fusion
- Predicting rRNA-, RNA-, and DNA-binding proteins from primary structure with support vector machines
- Prediction of the subcellular location of apoptosis proteins
- Prediction of apoptosis protein subcellular location using improved hybrid approach and pseudo-amino acid composition
- Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes
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