CONSeQuence: Prediction of reference peptides for absolute quantitative proteomics using consensus machine learning approaches

Molecular and Cellular Proteomics

Volumn 10, Issue 11, 2011, Pages

  Eyers, Claire E ^a   Lawless, Craig ^a   Wedge, David C ^a   Lau, King Wai ^a   Gaskell, Simon J ^a   Hubbard, Simon J ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

PEPTIDE;

AMINO ACID SEQUENCE; ANALYTIC METHOD; ARTICLE; CONSEQUENCE; ELECTROSPRAY; HYDROPHOBICITY; LIQUID CHROMATOGRAPHY; MACHINE LEARNING; PEPTIDE ANALYSIS; PHYSICAL CHEMISTRY; PREDICTION; PRIORITY JOURNAL; PROTEOMICS; QUANTITATIVE ANALYSIS; SENSITIVITY ANALYSIS; YEAST;

AMINO ACID SEQUENCE; ARTIFICIAL INTELLIGENCE; CAENORHABDITIS ELEGANS PROTEINS; COMPUTER SIMULATION; DROSOPHILA PROTEINS; MASS SPECTROMETRY; MODELS, MOLECULAR; NEURAL NETWORKS (COMPUTER); PEPTIDES; PROTEOME; PROTEOMICS; REFERENCE STANDARDS; SACCHAROMYCES CEREVISIAE PROTEINS; SUPPORT VECTOR MACHINES;

EID: 80053074684     PISSN: 15359476     EISSN: 15359484     Source Type: Journal    
DOI: 10.1074/mcp.M110.003384     Document Type: Article

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