Predicting sulfotyrosine sites using the random forest algorithm with significantly improved prediction accuracy

BMC Bioinformatics

Volumn 10, Issue , 2009, Pages 361-

  Yang, Zheng R ^a  

^a UNIVERSITY OF EXETER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

AMINO ACIDS; DECISION TREES; HIDDEN MARKOV MODELS; HYDROPHOBICITY; LEARNING ALGORITHMS; MACHINE LEARNING; NEURAL NETWORKS; PEPTIDES; SUPPORT VECTOR MACHINES;

ACCURATE PREDICTION; DISEASE DEVELOPMENT; HYDROPHOBICITY SCALE; PEPTIDE CLASSIFICATION; POST-TRANSLATIONAL MODIFICATIONS; PREDICTION ACCURACY; RANDOM FOREST ALGORITHM; RANDOM FOREST MODELING;

FORECASTING;

SULFOTYROSINE; TYROSINE; UNCLASSIFIED DRUG;

ACCURACY; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER PROGRAM; CONTROLLED STUDY; HIDDEN MARKOV MODEL; HYDROPHOBICITY; INTERMETHOD COMPARISON; MACHINE LEARNING; NUCLEOTIDE SEQUENCE; PEPTIDE ANALYSIS; PREDICTION; RANDOM FOREST ALGORITHM; SENSITIVITY AND SPECIFICITY; SULFATION;

EID: 70749137265     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-10-361     Document Type: Article

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