- author = "Samaneh Azari and Bing Xue and Mengjie Zhang and Lifeng Peng",
- title = "Learning to Rank Peptide-Spectrum Matches Using Genetic Programming",
- booktitle = "2019 IEEE Congress on Evolutionary Computation, CEC 2019",
- year = "2019",
- editor = "Carlos A. Coello Coello",
- pages = "3244--3251",
- address = "Wellington, New Zealand",
- month = "10-13 " # jun,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- keywords = "genetic algorithms, genetic programming, ranking function, peptide-spectrum match, tandem mass spectrometry",
- isbn13 = "978-1-7281-2152-6",
-
DOI = "
doi:10.1109/CEC.2019.8790049",
- size = "8 pages",
- abstract = "The analysis of tandem mass spectrometry (MS/MS) proteomics data relies on automated methods that assign peptides to observed MS/MS spectra. Typically these methods return a list of candidate peptide-spectrum matches (PSMs), ranked according to a scoring function. Normally the highest-scoring candidate peptide is considered as the best match for each spectrum. However, these best matches do not necessary always indicate the true matches. Identifying a full-length correct peptide by peptide identification tools is crucial, and we do not want to assign a spectrum to the peptide which is not expressed in the given biological sample. Therefore in this paper, we present a new approach to improving the previous ordering/ranking of the PSMs, aiming at bringing the correct PSM for spectrum ahead of all the incorrect ones for the same spectrum. We develop a new method called GP-PSM-rank, which employs genetic programming (GP) to learn a ranking function by combining different feature functions",
-
notes = "also known as \cite{8790049}
IEEE Catalog Number: CFP19ICE-ART",
