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  pages = {1583--1585},
  groups = {Proteomics}
}

@article{Smith2014,
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@article{Qi2014,
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  issn = {16159853},
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@article{Helsens2010,
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@article{Stephan2010,
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}

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}

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@article{Martens2011,
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@inproceedings{Micenkova2013,
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  title = {Explaining Outliers by Subspace Separability},
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  doi = {10.1109/ICDM.2013.132},
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@article{Bittremieux2015,
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}

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@thesis{Vu2014b,
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  title = {Intelligent Analysis and Data Mining of Spectral Data in Proteomics and Metabolomics},
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  pagetotal = {131},
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@inproceedings{Caruana2006,
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  title = {An Empirical Comparison of Supervised Learning Algorithms},
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  title = {Data Mining in Metric Space: {{An}} Empirical Analysis of Supervised Learning Performance Criteria},
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  booktitle = {Proceedings of the {{Tenth ACM SIGKDD International Conference}} on {{Knowledge Discovery}} and {{Data Mining}} - {{KDD}} '04},
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@article{Broeckling2013,
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@book{zotero-null-3864,
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@article{Zhang2015,
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@article{Palmer2015,
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@thesis{zotero-null-3910,
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  institution = {{Universiteit Antwerpen}},
  author = {Verstrepen, Koen},
  date = {2015-12-21},
  groups = {Machine learning}
}

@article{Staes2013,
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@article{Kuljanin2017,
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@article{Zolg2017,
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  date = {2017-01-30},
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}

@article{Glibert2014,
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  volume = {14},
  issn = {16159853},
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  number = {6},
  journaltitle = {PROTEOMICS},
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}

@article{Baker2017a,
  title = {Scientific Computing: {{Code}} Alert},
  volume = {541},
  issn = {0028-0836, 1476-4687},
  doi = {10.1038/nj7638-563a},
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@article{Altman2017,
  title = {Points of Significance: {{Interpreting P}} Values},
  volume = {14},
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@article{Perez-Riverol2017,
  title = {Synthetic Human Proteomes for Accelerating Protein Research},
  volume = {14},
  issn = {1548-7091, 1548-7105},
  doi = {10.1038/nmeth.4191},
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  journaltitle = {Nat. Methods},
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  date = {2017-02-28},
  pages = {240--242},
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@article{Johnson2017,
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1702.08734},
  title = {Billion-Scale Similarity Search with {{GPUs}}},
  url = {http://arxiv.org/abs/1702.08734},
  journaltitle = {ArXiv170208734 Cs},
  author = {Johnson, Jeff and Douze, Matthijs and Jégou, Hervé},
  date = {2017-02-28},
  groups = {Machine learning}
}

@article{Li2017,
  title = {Global Post-Translational Modification Discovery},
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@article{Dunn2017,
  title = {Quality Assurance and Quality Control Processes: {{Summary}} of a Metabolomics Community Questionnaire},
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  issn = {1573-3882, 1573-3890},
  doi = {10.1007/s11306-017-1188-9},
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@article{May2017,
  title = {Param-{{Medic}}: {{A}} Tool for Improving {{MS}}/{{MS}} Database Search Yield by Optimizing Parameter Settings},
  volume = {16},
  issn = {1535-3893, 1535-3907},
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@article{DeNeuter2017,
  title = {On the Feasibility of Mining {{CD8}}+ {{T}}-Cell Receptor Patterns Underlying Immunogenic Peptide Recognition.},
  volume = {Early view},
  issn = {0093-7711, 1432-1211},
  doi = {10.1007/s00251-017-1023-5},
  journaltitle = {Immunogenetics},
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  date = {2017-08-04},
  groups = {Publications}
}

@thesis{Bittremieux2017a,
  location = {{Antwerp, Belgium}},
  title = {Computational Solutions for Quality Control of Mass Spectrometry-Based Proteomics},
  url = {http://hdl.handle.net/10067/1415590151162165141},
  pagetotal = {129},
  institution = {{Universiteit Antwerpen}},
  author = {Bittremieux, Wout},
  date = {2017-02-24},
  groups = {Publications}
}

@article{Kong2017,
  title = {{{MSFragger}}: {{Ultrafast}} and Comprehensive Peptide Identification in Mass Spectrometry–based Proteomics},
  issn = {1548-7091, 1548-7105},
  doi = {10.1038/nmeth.4256},
  journaltitle = {Nat. Methods},
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  date = {2017-04-10},
  groups = {Proteomics}
}

@article{Burke2017,
  title = {The Hybrid Search: {{A}} Mass Spectral Library Search Method for Discovery of Modifications in Proteomics},
  issn = {1535-3893, 1535-3907},
  doi = {10.1021/acs.jproteome.6b00988},
  journaltitle = {J. Proteome Res.},
  author = {Burke, Meghan C and Mirokhin, Yuri A and Tchekhovskoi, Dmitrii V and Markey, Sanford P and Heidbrink Thompson, Jenny and Larkin, Christopher and Stein, Stephen E},
  date = {2017-04-11},
  groups = {Proteomics}
}

@article{Smith2017,
  archivePrefix = {arXiv},
  eprinttype = {arxiv},
  eprint = {1704.01568},
  title = {Best Practices for Applying Deep Learning to Novel Applications},
  url = {http://arxiv.org/abs/1704.01568},
  journaltitle = {ArXiv170401568 Cs},
  author = {Smith, Leslie N},
  date = {2017-04-05},
  groups = {Deep learning}
}

@book{McDowell2015,
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  groups = {Machine learning \& statistics}
}

@book{Murphy2012,
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  pagetotal = {1067},
  publisher = {{MIT Press}},
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  groups = {Machine learning}
}

@book{Bishop2006,
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@article{Gupta2017,
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}

@article{Plasman2017,
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@article{Esteva2017,
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@article{Gulshan2016,
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@article{Graves2013,
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@article{Willems2017,
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}

@article{Bittremieux2017_ASMS_smartlab,
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@article{Bittremieux2017_ASMS_dl,
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}

@article{Mitulovic2006,
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  author = {Mitulović, Goran and Mechtler, Karl},
  date = {2006-05-10},
  pages = {249--260},
  groups = {Proteomics}
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@article{Mitulovic2003,
  title = {An Improved Method for Tracking and Reducing the Void Volume in Nano {{HPLC}}--{{MS}} with Micro Trapping Columns},
  volume = {376},
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}

@article{Noga2007,
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@article{Maes2014,
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}

@article{Lagerwerf1996,
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@article{Jenkins2015,
  title = {Recommendations for Validation of {{LC}}-{{MS}}/{{MS}} Bioanalytical Methods for Protein Biotherapeutics},
  volume = {17},
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}

@article{Zhou2015,
  title = {Performance Metrics for Evaluating System Suitability in Liquid Chromatography-Mass Spectrometry Peptide Mass Mapping of Protein Therapeutics and Monoclonal Antibodies},
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}

@article{Grant2014,
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@article{Hoofnagle2015,
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@article{Noble2017,
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@book{Leek2016,
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@article{Dogu2017,
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@article{M?ller2017,
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@article{Vizcaino2017,
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@book{Arndt2011,
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@book{Bentley2000,
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@book{Warren2012,
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@book{Cover2006,
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@article{Blei2017,
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@inproceedings{Feremans2017,
  location = {{Cleveland, Ohio, USA}},
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  booktitle = {Proceedings of the {{ASME}} 2017 {{International Design Engineering Technical Conferences}}  {{Computers}} and {{Information}} in {{Engineering}} - {{IDETC}} 2017},
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@article{Olson2017,
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@article{Deutsch2017,
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@article{Hall2014,
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@article{Bilbao2015,
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