Improvements to the percolator algorithm for Peptide identification from shotgun proteomics data sets

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Fältnamn	Indikatorer	Metadata
000		03116naa a2200397 4500
001		oai:DiVA.org:kth-48844
003		SwePub
008		111123s2009 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
009		oai:DiVA.org:su-34702
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-488442 URI
024	7	a https://doi.org/10.1021/pr801109k2 DOI
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-347022 URI
040		a (SwePub)kthd (SwePub)su
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a art2 swepub-publicationtype
100	1	a Spivak, Marina4 aut
245	1 0	a Improvements to the percolator algorithm for Peptide identification from shotgun proteomics data sets
264		c 2009-05-19
264	1	b American Chemical Society (ACS),c 2009
338		a print2 rdacarrier
500		a QC 20111128
520		a Shotgun proteomics coupled with database search software allows the identification of a large number of peptides in a single experiment. However, some existing search algorithms, such as SEQUEST, use score functions that are designed primarily to identify the best peptide for a given spectrum. Consequently, when comparing identifications across spectra, the SEQUEST score function Xcorr fails to discriminate accurately between correct and incorrect peptide identifications. Several machine learning methods have been proposed to address the resulting classification task of distinguishing between correct and incorrect peptide-spectrum matches (PSMs). A recent example is Percolator, which uses semisupervised learning and a decoy database search strategy to learn to distinguish between correct and incorrect PSMs identified by a database search algorithm. The current work describes three improvements to Percolator. (1) Percolator's heuristic optimization is replaced with a clear objective function, with intuitive reasons behind its choice. (2) Tractable nonlinear models are used instead of linear models, leading to improved accuracy over the original Percolator. (3) A method, Q-ranker, for directly optimizing the number of identified spectra at a specified q value is proposed, which achieves further gains.
650	7	a NATURVETENSKAPx Data- och informationsvetenskapx Bioinformatik0 (SwePub)102032 hsv//swe
650	7	a NATURAL SCIENCESx Computer and Information Sciencesx Bioinformatics0 (SwePub)102032 hsv//eng
700	1	a Weston, Jason4 aut
700	1	a Bottou, Léon4 aut
700	1	a Käll, Lukas,d 1969-u Stockholms universitet,Institutionen för biokemi och biofysik4 aut0 (Swepub:su)lkl
700	1	a Noble, William Stafford4 aut
710	2	a Stockholms universitetb Institutionen för biokemi och biofysik4 org
773	0	t Journal of Proteome Researchd : American Chemical Society (ACS)g 8:7, s. 3737-3745q 8:7<3737-3745x 1535-3893x 1535-3907
856	4	u https://europepmc.org/articles/pmc2710313?pdf=render
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-48844
856	4 8	u https://doi.org/10.1021/pr801109k
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-34702

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Av författaren/redakt...: Spivak, Marina; Weston, Jason; Bottou, Léon; Käll, Lukas, 196 ...; Noble, William S ...

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Av lärosätet: Kungliga Tekniska Högskolan; Stockholms universitet

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