Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics

BMC Bioinformatics

Volumn 9, Issue , 2008, Pages

  Timm, Wiebke ^a   Scherbart, Alexandra ^a   Böcker, Sebastian ^b   Kohlbacher, Oliver ^c   Nattkemper, Tim W ^a  

^a BIELEFELD UNIVERSITY   (Germany)

^b FRIEDRICH SCHILLER UNIVERSITY JENA   (Germany)

^c UNIVERSITY OF TÜBINGEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

FORWARD FEATURE SELECTIONS; MACHINE LEARNING METHODS; MACHINE LEARNING TECHNIQUES; MALDI-TOF MASS SPECTROMETRY; MASS SPECTROMETRIC ANALYSIS; PHYSICOCHEMICAL PROPERTY; QUANTITATIVE PROTEOMICS; SUPPORT VECTOR REGRESSION (SVR);

CORRELATION METHODS; FACSIMILE; FORECASTING; MASS SPECTROMETERS; MASS SPECTROMETRY; MOLECULAR BIOLOGY; PEPTIDES;

LEARNING SYSTEMS;

ARTICLE; CONTROLLED STUDY; CORYNEBACTERIUM GLUTAMICUM; DATA EXTRACTION; INTERMETHOD COMPARISON; MACHINE LEARNING; MATRIX ASSISTED LASER DESORPTION IONIZATION TIME OF FLIGHT MASS SPECTROMETRY; NONHUMAN; PHYSICAL CHEMISTRY; PREDICTION; PROTEOMICS; QUANTITATIVE ANALYSIS; SUPPORT VECTOR MACHINE; ALGORITHM; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; CHEMISTRY; MASS SPECTROMETRY; METHODOLOGY; STATISTICAL MODEL;

PEPTIDE; PROTEIN;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; LINEAR MODELS; NEURAL NETWORKS (COMPUTER); PEPTIDES; PROTEINS; PROTEOMICS; SPECTROMETRY, MASS, MATRIX-ASSISTED LASER DESORPTION-IONIZATION;

EID: 57649238001     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-9-443     Document Type: Article

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