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Machine learning methods for prediction of disulphide bonding states of cysteine residues in proteins
Priyank Shukla
School of Biomedical Sciences
Faculty Of Life & Health Sciences
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peer-review
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Dive into the research topics of 'Machine learning methods for prediction of disulphide bonding states of cysteine residues in proteins'. Together they form a unique fingerprint.
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Biochemistry, Genetics and Molecular Biology
Disulfide Bond
100%
Cysteine
100%
Eukaryote
33%
Prokaryote
33%
Protein Folding
11%
Subcellular Localization
11%
Protein Structure
11%
Artificial Neural Network
11%
Signal Peptide
11%
Amino Acid Composition
11%
Hidden Markov Model
11%
Protein Engineering
11%
Protein Structure Prediction
11%
Material Science
Amino Acids
100%
Hidden Markov Model
100%
Phase Composition
100%
Neuroscience
Cysteine
100%
Protein Structure
22%
Amino Acid
11%
Hidden Markov Model
11%
Neural Network
11%
Protein Folding
11%
Signal Peptide
11%
Protein Engineering
11%
Artificial Neural Network
11%