Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses
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Publication:1715312
DOI10.1016/j.jtbi.2009.11.016zbMath1406.92455OpenAlexW2003613304WikidataQ35016043 ScholiaQ35016043MaRDI QIDQ1715312
Sasan Mohsenzadeh, Hassan Mohabatkar, Maryam Esmaeili
Publication date: 4 February 2019
Published in: Journal of Theoretical Biology (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jtbi.2009.11.016
Applications of statistics to biology and medical sciences; meta analysis (62P10) Protein sequences, DNA sequences (92D20)
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Uses Software
Cites Work
- Prediction of protein structural classes by recurrence quantification analysis based on chaos game representation
- Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern
- Predicting protein structural class based on multi-features fusion
- Predicting protein structural classes with pseudo amino acid composition: an approach using geometric moments of cellular automaton image
- Using stacked generalization to predict membrane protein types based on pseudo-amino acid composition
- Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach