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Items: 1 to 20 of 103

1.

Predicting intensity ranks of peptide fragment ions.

Frank AM.

J Proteome Res. 2009 May;8(5):2226-40. doi: 10.1021/pr800677f.

2.

A ranking-based scoring function for peptide-spectrum matches.

Frank AM.

J Proteome Res. 2009 May;8(5):2241-52. doi: 10.1021/pr800678b.

3.

MS2PIP: a tool for MS/MS peak intensity prediction.

Degroeve S, Martens L.

Bioinformatics. 2013 Dec 15;29(24):3199-203. doi: 10.1093/bioinformatics/btt544. Epub 2013 Sep 27.

4.

Evaluating de novo sequencing in proteomics: already an accurate alternative to database-driven peptide identification?

Muth T, Renard BY.

Brief Bioinform. 2018 Sep 28;19(5):954-970. doi: 10.1093/bib/bbx033.

PMID:
28369237
5.

PepNovo: de novo peptide sequencing via probabilistic network modeling.

Frank A, Pevzner P.

Anal Chem. 2005 Feb 15;77(4):964-73.

PMID:
15858974
6.

Exploiting the kernel trick to correlate fragment ions for peptide identification via tandem mass spectrometry.

Fu Y, Yang Q, Sun R, Li D, Zeng R, Ling CX, Gao W.

Bioinformatics. 2004 Aug 12;20(12):1948-54. Epub 2004 Mar 25.

PMID:
15044235
7.

A machine learning approach to explore the spectra intensity pattern of peptides using tandem mass spectrometry data.

Zhou C, Bowler LD, Feng J.

BMC Bioinformatics. 2008 Jul 30;9:325. doi: 10.1186/1471-2105-9-325.

8.

HI-bone: a scoring system for identifying phenylisothiocyanate-derivatized peptides based on precursor mass and high intensity fragment ions.

Perez-Riverol Y, Sánchez A, Noda J, Borges D, Carvalho PC, Wang R, Vizcaíno JA, Betancourt L, Ramos Y, Duarte G, Nogueira FC, González LJ, Padrón G, Tabb DL, Hermjakob H, Domont GB, Besada V.

Anal Chem. 2013 Apr 2;85(7):3515-20. doi: 10.1021/ac303239g. Epub 2013 Mar 20.

PMID:
23448308
9.

Context-sensitive markov models for peptide scoring and identification from tandem mass spectrometry.

Grover H, Wallstrom G, Wu CC, Gopalakrishnan V.

OMICS. 2013 Feb;17(2):94-105. doi: 10.1089/omi.2012.0073. Epub 2013 Jan 5.

10.

SQID: an intensity-incorporated protein identification algorithm for tandem mass spectrometry.

Li W, Ji L, Goya J, Tan G, Wysocki VH.

J Proteome Res. 2011 Apr 1;10(4):1593-602. doi: 10.1021/pr100959y. Epub 2011 Feb 23.

11.

ETISEQ--an algorithm for automated elution time ion sequencing of concurrently fragmented peptides for mass spectrometry-based proteomics.

Wong JW, Schwahn AB, Downard KM.

BMC Bioinformatics. 2009 Aug 10;10:244. doi: 10.1186/1471-2105-10-244.

12.

DirecTag: accurate sequence tags from peptide MS/MS through statistical scoring.

Tabb DL, Ma ZQ, Martin DB, Ham AJ, Chambers MC.

J Proteome Res. 2008 Sep;7(9):3838-46. doi: 10.1021/pr800154p. Epub 2008 Jul 17.

13.

Intensity-based protein identification by machine learning from a library of tandem mass spectra.

Elias JE, Gibbons FD, King OD, Roth FP, Gygi SP.

Nat Biotechnol. 2004 Feb;22(2):214-9. Epub 2004 Jan 18.

PMID:
14730315
14.

Prediction of peptide fragment ion mass spectra by data mining techniques.

Dong NP, Liang YZ, Xu QS, Mok DK, Yi LZ, Lu HM, He M, Fan W.

Anal Chem. 2014 Aug 5;86(15):7446-54. doi: 10.1021/ac501094m. Epub 2014 Jul 25.

PMID:
25032905
15.

Optimization by infusion of multiple reaction monitoring transitions for sensitive quantification of peptides by liquid chromatography/mass spectrometry.

Alghanem B, Nikitin F, Stricker T, Duchoslav E, Luban J, Strambio-De-Castillia C, Muller M, Lisacek F, Varesio E, Hopfgartner G.

Rapid Commun Mass Spectrom. 2017 May 15;31(9):753-761. doi: 10.1002/rcm.7839.

PMID:
28199054
16.

Complexity and scoring function of MS/MS peptide de novo sequencing.

Xu C, Ma B.

Comput Syst Bioinformatics Conf. 2006:361-9.

17.

PI: an open-source software package for validation of the SEQUEST result and visualization of mass spectrum.

Qiao Y, Zhang H, Bu D, Sun S.

BMC Bioinformatics. 2011 Jun 15;12:234. doi: 10.1186/1471-2105-12-234.

18.

OpenMS-Simulator: an open-source software for theoretical tandem mass spectrum prediction.

Wang Y, Yang F, Wu P, Bu D, Sun S.

BMC Bioinformatics. 2015 Apr 2;16:110. doi: 10.1186/s12859-015-0540-1.

19.

Ranking Fragment Ions Based on Outlier Detection for Improved Label-Free Quantification in Data-Independent Acquisition LC-MS/MS.

Bilbao A, Zhang Y, Varesio E, Luban J, Strambio-De-Castillia C, Lisacek F, Hopfgartner G.

J Proteome Res. 2015 Nov 6;14(11):4581-93. doi: 10.1021/acs.jproteome.5b00394. Epub 2015 Oct 14.

20.

MS2PIP prediction server: compute and visualize MS2 peak intensity predictions for CID and HCD fragmentation.

Degroeve S, Maddelein D, Martens L.

Nucleic Acids Res. 2015 Jul 1;43(W1):W326-30. doi: 10.1093/nar/gkv542. Epub 2015 May 18.

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