Improved prediction of peptide detectability for targeted proteomics using a rank-based algorithm and organism-specific data

Journal of Proteomics

Volumn 108, Issue , 2014, Pages 269-283

  Qeli, Ermir ^a,d   Omasits, Ulrich ^a,b   Goetze, Sandra ^a,b   Stekhoven, Daniel J ^a   Frey, Juerg E ^c   Basler, Konrad ^a   Wollscheid, Bernd ^b   Brunner, Erich ^a   Ahrens, Christian H ^a,c  

^a UNIVERSITY OF ZURICH   (Switzerland)

^b ETH ZURICH   (Switzerland)

^c AGROSCOPE   (Switzerland)

^d SWISS REINSURANCE COMPANY   (Switzerland)

Author keywords

Machine learning; Peptide detectability; Proteotypic peptides; Rank prediction algorithms; SRM; Targeted proteomics

Indexed keywords

PEPTIDE; PROTEIN; BACTERIAL PROTEIN; DROSOPHILA PROTEIN; SACCHAROMYCES CEREVISIAE PROTEIN;

ACCURACY; ALGORITHM; ARTICLE; COMPUTER MODEL; DATA ANALYSIS; MACHINE LEARNING; ORGANISMS; PEPTIDE ANALYSIS; PREDICTION; PRIORITY JOURNAL; PROTEOMICS; SYSTEMS BIOLOGY; ANIMAL; BARTONELLA HENSELAE; DROSOPHILA MELANOGASTER; GENETICS; LEPTOSPIRA INTERROGANS; METABOLISM; PROCEDURES; PROTEIN DATABASE; SACCHAROMYCES CEREVISIAE; SEQUENCE ANALYSIS;

ALGORITHMS; ANIMALS; BACTERIAL PROTEINS; BARTONELLA HENSELAE; DATABASES, PROTEIN; DROSOPHILA MELANOGASTER; DROSOPHILA PROTEINS; LEPTOSPIRA INTERROGANS; PEPTIDES; SACCHAROMYCES CEREVISIAE; SACCHAROMYCES CEREVISIAE PROTEINS; SEQUENCE ANALYSIS, PROTEIN;

EID: 84902435672     PISSN: 18743919     EISSN: 18767737     Source Type: Journal    
DOI: 10.1016/j.jprot.2014.05.011     Document Type: Article

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