(Q38170020)

30 August 2017

title

Machine learning applications in proteomics research: how the past can boost the future (English)

1 reference

30 August 2017

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Wout Bittremieux

2

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Kris Laukens

object named as

Kris Laukens

6

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Lennart Martens

object named as

Lennart Martens

9

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Harald Barsnes

8

object named as

Harald Barsnes

1 reference

30 August 2017

Pieter Kelchtermans

1

1 reference

30 August 2017

Kurt De Grave

3

1 reference

30 August 2017

Sven Degroeve

4

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30 August 2017

Jan Ramon

5

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30 August 2017

Dirk Valkenborg

7

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30 August 2017

publication date

21 January 2014

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30 August 2017

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30 August 2017

volume

14

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30 August 2017

issue

4-5

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30 August 2017

page(s)

353-366

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Computational structural analysis of protein interactions and networks

30 August 2017

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21 January 2018

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21 January 2018

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21 January 2018

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21 January 2018

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

21 January 2018

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Combining results of multiple search engines in proteomics

21 January 2018

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Review of factors that influence the abundance of ions produced in a tandem mass spectrometer and statistical methods for discovering these factors

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

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On the accuracy and limits of peptide fragmentation spectrum prediction

21 January 2018

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Predicting intensity ranks of peptide fragment ions

21 January 2018

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Extending the coverage of spectral libraries: a neighbor-based approach to predicting intensities of peptide fragmentation spectra

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What does it mean to identify a protein in proteomics?

21 January 2018

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Interpretation of shotgun proteomic data: the protein inference problem

21 January 2018

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Mass spectrometry-based proteomics

21 January 2018

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Proteomics data validation: why all must provide data

21 January 2018

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False discovery rates of protein identifications: a strike against the two-peptide rule

21 January 2018

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A review of statistical methods for protein identification using tandem mass spectrometry

21 January 2018

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A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

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A statistical model for identifying proteins by tandem mass spectrometry

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Analysis and validation of proteomic data generated by tandem mass spectrometry

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A linear programming model for protein inference problem in shotgun proteomics

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21 January 2018

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Protein identification by spectral networks analysis

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21 January 2018

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Direct maximization of protein identifications from tandem mass spectra

21 January 2018

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Rapid and accurate peptide identification from tandem mass spectra

21 January 2018

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Accurate mass spectrometry based protein quantification via shared peptides

21 January 2018

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Protein identification and Peptide expression resolver: harmonizing protein identification with protein expression data

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Inference and validation of protein identifications

21 January 2018

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Interlaboratory study characterizing a yeast performance standard for benchmarking LC-MS platform performance.

21 January 2018

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Interlaboratory studies and initiatives developing standards for proteomics

21 January 2018

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A large synthetic peptide and phosphopeptide reference library for mass spectrometry-based proteomics

21 January 2018

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Data standardization and sharing-the work of the HUPO-PSI.

21 January 2018

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The importance of the digest: Proteolysis and absolute quantification in proteomics

21 January 2018

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Calculating absolute and relative protein abundance from mass spectrometry-based protein expression data

21 January 2018

1 reference

5 May 2024

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Retention time prediction using the model of liquid chromatography of biomacromolecules at critical conditions in LC-MS phosphopeptide analysis

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5 May 2024

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Computational approaches to protein inference in shotgun proteomics

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5 May 2024

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Assessment of PLSDA cross validation

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Getting intimate with trypsin, the leading protease in proteomics

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https://opencitations.net/index/api/v1/references/10.1002/PMIC.201300289

Experimental Protein Mixture for Validating Tandem Mass Spectral Analysis

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5 May 2024

https://opencitations.net/index/api/v1/references/10.1002/PMIC.201300289

Prediction of Chromatographic Retention and Protein Identification in Liquid Chromatography/Mass Spectrometry

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https://opencitations.net/index/api/v1/references/10.1002/PMIC.201300289

AMASS: software for automatically validating the quality of MS/MS spectrum from SEQUEST results

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5 May 2024

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Identifiers

DOI

10.1002/PMIC.201300289

1 reference

30 August 2017

1 reference