O. Serang and L. Kall, Solution to Statistical Challenges in Proteomics Is More Statistics, Not Less, Journal of Proteome Research, vol.14, issue.10, pp.4099-4103, 2015.
DOI : 10.1021/acs.jproteome.5b00568

P. Mallick and B. Kuster, Proteomics: a pragmatic perspective, Nature Biotechnology, vol.24, issue.7, pp.695-709, 2010.
DOI : 10.1038/nbt.1658

B. Canas, C. Pieiro, and E. Calvo, Trends in sample preparation for classical and second generation proteomics, Journal of Chromatography A, vol.1, issue.2, pp.235-258, 2007.

H. Mottaz-brewer, A. Norbeck, and J. Adkins, Optimization of Proteomic Sample Preparation Procedures for Comprehensive Protein Characterization of Pathogenic Systems, J Biomol Tech, vol.5, pp.285-295, 2008.

J. Wisniewski, A. Zougman, and M. Mann, Combination of FASP and StageTip-Based Fractionation Allows In-Depth Analysis of the Hippocampal Membrane Proteome, Journal of Proteome Research, vol.8, issue.12, pp.5674-5678, 2009.
DOI : 10.1021/pr900748n

P. Glibert, S. Van, and M. Dhaenens, iTRAQ as a method for optimization: Enhancing peptide recovery after gel fractionation, PROTEOMICS, vol.9, issue.6, pp.680-684, 2014.
DOI : 10.1002/pmic.201300444

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4413792

E. Vandermarliere, M. Mueller, and L. Martens, Getting intimate with trypsin, the leading protease in proteomics, Mass Spectrometry Reviews, vol.78, issue.Pt 1, pp.453-465, 2013.
DOI : 10.1002/mas.21376

J. Meyer, S. Kim, and D. Maltby, Expanding proteome coverage with orthogonalspecificity alpha-lytic proteases, Mol Cell Proteomics, vol.3, pp.823-835, 2014.
DOI : 10.1074/mcp.m113.034710

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3945911

S. Saveliev, M. Bratz, and R. Zubarev, Trypsin/Lys-C protease mix for enhanced protein mass spectrometry analysis, Nat Meth, vol.11, 2013.

D. Swaney, C. Wenger, and J. Coon, Value of Using Multiple Proteases for Large-Scale Mass Spectrometry-Based Proteomics, Journal of Proteome Research, vol.9, issue.3, pp.1323-1329, 2010.
DOI : 10.1021/pr900863u

S. Thakur, T. Geiger, and B. Chatterjee, Deep and Highly Sensitive Proteome Coverage by LC-MS/MS Without Prefractionation, Molecular & Cellular Proteomics, vol.10, issue.8, 2011.
DOI : 10.1074/mcp.M110.003699

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3149084

N. Nagaraj, N. Kulak, and J. Cox, System-wide Perturbation Analysis with Nearly Complete Coverage of the Yeast Proteome by Single-shot Ultra HPLC Runs on a Bench Top Orbitrap, Molecular & Cellular Proteomics, vol.11, issue.3, 2012.
DOI : 10.1074/mcp.M111.013722

F. Ahmed, Liquid chromatography???mass spectrometry: a tool for proteome analysis and biomarker discovery and validation, Expert Opinion on Medical Diagnostics, vol.19, issue.4, pp.429-444, 2009.
DOI : 10.1021/pr700775x

E. Mostovenko, C. Hassan, and J. Rattke, Comparison of peptide and protein fractionation methods in proteomics, EuPA Open Proteomics, vol.1, pp.30-37, 2013.
DOI : 10.1016/j.euprot.2013.09.001

P. Meysman, K. Titeca, and S. Eyckerman, Protein complex analysis: From raw protein lists to protein interaction networks, Mass Spectrometry Reviews, vol.490, 2015.
DOI : 10.1002/mas.21485

R. Millioni, S. Tolin, and L. Puricelli, High Abundance Proteins Depletion vs Low Abundance Proteins Enrichment: Comparison of Methods to Reduce the Plasma Proteome Complexity, PLoS ONE, vol.5, issue.5, p.19603, 2011.
DOI : 10.1371/journal.pone.0019603.s003

K. Smolders, N. Lombaert, and D. Valkenborg, An effective plasma membrane proteomics approach for small tissue samples, Scientific Reports, vol.6, 2015.
DOI : 10.1038/srep10917

URL : http://doi.org/10.1038/srep10917

S. Klie, L. Martens, and J. Vizcaino, Analyzing Large-Scale Proteomics Projects with Latent Semantic Indexing, Journal of Proteome Research, vol.7, issue.1, pp.182-191, 2008.
DOI : 10.1021/pr070461k

S. Gallien, E. Duriez, and K. Demeure, Selectivity of LC-MS/MS analysis: Implication for proteomics experiments, Journal of Proteomics, vol.81, pp.148-158, 2013.
DOI : 10.1016/j.jprot.2012.11.005

S. Gallien and B. Domon, Advances in high-resolution quantitative proteomics: implications for clinical applications, Expert Review of Proteomics, vol.12, issue.5, pp.489-498, 2015.
DOI : 10.1586/14789450.2015.1069188

Y. Kim, S. Gallien, and O. Van, Targeted proteomics strategy applied to biomarker evaluation, PROTEOMICS - Clinical Applications, vol.56, issue.Suppl 16, pp.11-12739, 2013.
DOI : 10.1002/prca.201300070

B. Domon and R. Aebersold, Options and considerations when selecting a quantitative proteomics strategy, Nature Biotechnology, vol.7, issue.7, pp.710-721, 2010.
DOI : 10.1038/nbt.1661

J. Wisniewski, K. Dus, and M. Mann, Proteomic workflow for analysis of archival formalinfixed and paraffin-embedded clinical samples to a depth of 10 000 proteins, Proteomics Clin.Appl, vol.3, issue.4, pp.225-233, 2013.

J. Egertson, A. Kuehn, and G. Merrihew, Multiplexed MS/MS for improved dataindependent acquisition, Nat Methods, vol.8, pp.744-746, 2013.
DOI : 10.1038/nmeth.2528

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3881977

J. Egertson, A. Kuehn, and G. Merrihew, Multiplexed MS/MS for improved dataindependent acquisition, Nat Methods, vol.8, pp.744-746, 2013.
DOI : 10.1038/nmeth.2528

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3881977

H. Rost, G. Rosenberger, and P. Navarro, OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data, Nature Biotechnology, vol.10, issue.3, pp.219-223, 2014.
DOI : 10.1186/1471-2105-9-163

L. Gillet, P. Navarro, and S. Tate, Targeted Data Extraction of the MS/MS Spectra Generated by Data-independent Acquisition: A New Concept for Consistent and Accurate Proteome Analysis, Molecular & Cellular Proteomics, vol.11, issue.6, p.111, 2012.
DOI : 10.1074/mcp.O111.016717

M. Vaudel, A. Sickmann, and L. Martens, Peptide and protein quantification: A map of the minefield, PROTEOMICS, vol.7, issue.4, pp.650-670, 2010.
DOI : 10.1002/pmic.200900481

A. Thompson, J. Schafer, and K. Kuhn, Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS, Anal.Chem, vol.8, pp.1895-1904, 2003.

P. Chong, C. Gan, and T. Pham, Isobaric Tags for Relative and Absolute Quantitation (iTRAQ) Reproducibility:?? Implication of Multiple Injections, Journal of Proteome Research, vol.5, issue.5, pp.1232-1240, 2006.
DOI : 10.1021/pr060018u

S. Ong and M. Mann, A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC), Nature Protocols, vol.3, issue.6, pp.2650-2660, 2006.
DOI : 10.1038/nprot.2006.427

S. Gerber, J. Rush, and O. Stemman, Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS, Proceedings of the National Academy of Sciences, vol.100, issue.12, pp.6940-6945, 2003.
DOI : 10.1073/pnas.0832254100

P. Brownridge, V. Harman, and D. Simpson, Absolute Multiplexed Protein Quantification Using QconCAT Technology, Methods Mol Biol, pp.267-293, 2012.
DOI : 10.1007/978-1-61779-885-6_18

Y. Zhang, B. Fonslow, and B. Shan, Protein Analysis by Shotgun/Bottom-up Proteomics, Chemical Reviews, vol.113, issue.4, pp.2343-2394, 2013.
DOI : 10.1021/cr3003533

M. Vaudel, J. Burkhart, and S. Radau, Integral Quantification Accuracy Estimation for Reporter Ion-based Quantitative Proteomics (iQuARI), Journal of Proteome Research, vol.11, issue.10, pp.5072-5080, 2012.
DOI : 10.1021/pr300247u

A. Christoforou and K. Lilley, Taming the isobaric tagging elephant in the room in quantitative proteomics, Nature Methods, vol.8, issue.11, pp.911-913, 2011.
DOI : 10.1021/pr900451u

S. Nahnsen and O. Kohlbacher, In silico design of targeted SRM-based experiments, BMC Bioinformatics, vol.13, issue.Suppl 16, 2012.
DOI : 10.1021/pr1001803

P. Picotti and R. Aebersold, Selected reaction monitoring???based proteomics: workflows, potential, pitfalls and future directions, Nature Methods, vol.56, issue.6, pp.555-566, 2012.
DOI : 10.1038/nmeth.2015

R. Zubarev and M. Mann, On the Proper Use of Mass Accuracy in Proteomics, Molecular & Cellular Proteomics, vol.6, issue.3, pp.377-381, 2007.
DOI : 10.1074/mcp.M600380-MCP200

Y. Zhang, Z. Wen, and M. Washburn, Effect of Dynamic Exclusion Duration on Spectral Count Based Quantitative Proteomics, Analytical Chemistry, vol.81, issue.15, pp.6317-6326, 2009.
DOI : 10.1021/ac9004887

J. Vissers, R. Blackburn, and M. Moseley, A novel Interface for variable flow nanoscale LC/MS/MS for improved proteome coverage, Journal of the American Society for Mass Spectrometry, vol.276, issue.7, pp.760-771, 2002.
DOI : 10.1016/S1044-0305(02)00418-X

T. Batth, P. Singh, and V. Ramakrishnan, A targeted proteomics toolkit for highthroughput absolute quantification of Escherichia coli proteins, Metab Eng, pp.48-56, 2014.

Y. Karpievitch, A. Dabney, and R. Smith, Normalization and missing value imputation for label-free LC-MS analysis, BMC Bioinformatics, vol.13, issue.Suppl 16, 2012.
DOI : 10.1186/gb-2006-7-3-401

URL : http://doi.org/10.1186/1471-2105-13-s16-s5

X. Lai, L. Wang, and F. Witzmann, Issues and Applications in Label-Free Quantitative Mass Spectrometry, International Journal of Proteomics, vol.5, issue.3, 2013.
DOI : 10.1002/pmic.200900521

URL : http://doi.org/10.1155/2013/756039

J. Han, L. Ye, and L. Xu, Towards high peak capacity separations in normal pressure nanoflow liquid chromatography using meter long packed capillary columns, Analytica Chimica Acta, vol.852, pp.267-273, 2014.
DOI : 10.1016/j.aca.2014.09.006

R. Aebersold and M. Mann, Mass spectrometry-based proteomics, Nature, vol.1, issue.6928, pp.198-207, 2003.
DOI : 10.1016/S0960-9822(01)00632-7

R. Mee, A comprehensive guide to factorial two-level experimentation, 2009.
DOI : 10.1007/b105081

A. Dean and S. Lewis, Screening: methods for experimentation in industry, drug discovery, and genetics, 2006.
DOI : 10.1007/0-387-28014-6

A. Oberg and O. Vitek, Statistical Design of Quantitative Mass Spectrometry-Based Proteomic Experiments, Journal of Proteome Research, vol.8, issue.5, pp.2144-2156, 2009.
DOI : 10.1021/pr8010099

N. Karp and K. Lilley, Design and Analysis Issues in Quantitative Proteomics Studies, PROTEOMICS, vol.16, issue.S1, pp.42-50, 2007.
DOI : 10.1002/pmic.200700683

M. Vaudel, H. Barsnes, and L. Martens, Bioinformatics for Proteomics: Opportunities at the Interface Between the Scientists, Their Experiments, and the Community, pp.239-248, 2014.
DOI : 10.1007/978-1-4939-0685-7_16

C. Kendziorski, R. Irizarry, and K. Chen, On the utility of pooling biological samples in microarray experiments, Proceedings of the National Academy of Sciences, vol.102, issue.12, pp.4252-4257, 2005.
DOI : 10.1073/pnas.0500607102

X. Peng, C. Wood, and E. Blalock, Statistical implications of pooling RNA samples for microarray experiments, BMC Bioinformatics, vol.26, 2003.

A. Diz, M. Truebano, and D. Skibinski, The consequences of sample pooling in proteomics: An empirical study, ELECTROPHORESIS, vol.8, issue.17, pp.2967-2975, 2009.
DOI : 10.1002/elps.200900210

N. Karp, M. Spencer, and H. Lindsay, Impact of Replicate Types on Proteomic Expression Analysis, Journal of Proteome Research, vol.4, issue.5, pp.1867-1871, 2005.
DOI : 10.1021/pr050084g

M. Raji and K. Schug, Chemometric study of the influence of instrumental parameters on ESI-MS analyte response using full factorial design, International Journal of Mass Spectrometry, vol.279, issue.2-3, pp.100-106, 2009.
DOI : 10.1016/j.ijms.2008.10.013

G. Andrews, R. Dean, and A. Hawkridge, Improving Proteome Coverage on a LTQ-Orbitrap Using Design of Experiments, Journal of The American Society for Mass Spectrometry, vol.21, issue.9, pp.773-783, 2011.
DOI : 10.1007/s13361-011-0075-2

A. Prieto, O. Zuloaga, and A. Usobiaga, Development of a stir bar sorptive extraction and thermal desorption???gas chromatography???mass spectrometry method for the simultaneous determination of several persistent organic pollutants in water samples, Journal of Chromatography A, vol.1174, issue.1-2, pp.40-49, 2007.
DOI : 10.1016/j.chroma.2007.07.054

R. Sutton, Reinforcement Learning: An Introduction, IEEE Transactions on Neural Networks, vol.9, issue.5, 1998.
DOI : 10.1109/TNN.1998.712192

P. Auer, N. Cesa-bianchi, and P. Fischer, Finite-time Analysis of the Multiarmed Bandit Problem, pp.235-256, 2002.

D. Jones, M. Schonlau, and W. Welch, Efficient Global Optimization of Expensive Black-Box Functions, pp.455-492, 1998.

D. Jones, A Taxonomy of Global Optimization Methods Based on Response Surfaces, pp.345-383, 2001.

T. Santner, The Design and analysis of computer experiments, 2003.
DOI : 10.1007/978-1-4757-3799-8

J. Knowles, ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems, IEEE Transactions on Evolutionary Computation, vol.10, issue.1, pp.50-66, 2006.
DOI : 10.1109/TEVC.2005.851274

D. Gorissen, I. Couckuyt, and P. Demeester, A Surrogate Modeling and Adaptive Sampling Toolbox for Computer Based Design, pp.2051-2055, 2010.

A. Forrester and A. Keane, Recent advances in surrogate-based optimization, Progress in Aerospace Sciences, vol.45, issue.1-3, pp.50-79, 2009.
DOI : 10.1016/j.paerosci.2008.11.001

D. Verbeeck, F. Maes, D. Grave, and K. , Multi-objective optimization with surrogate trees, Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference, GECCO '13, 2013.
DOI : 10.1145/2463372.2463455

URL : https://lirias.kuleuven.be/bitstream/123456789/392678/2/t08pap504-verbeeck.pdf

K. De-grave, J. Ramon, D. Raedt, and L. , Active Learning for High Throughput Screening, pp.185-196, 2008.
DOI : 10.1007/978-3-540-88411-8_19

W. Dunham, B. Larsen, and S. Tate, A cost?Çôbenefit analysis of multidimensional fractionation of affinity purification-mass spectrometry samples, Proteomics, vol.13, pp.2603-2612, 2011.

H. Sherwood, C. Gafken, P. Martin, and D. , Collision energy optimization of b-and yions for multiple reaction monitoring mass spectrometry, J Proteome Res, vol.1, pp.231-240, 2011.

P. Kelchtermans, W. Bittremieux, D. Grave, and K. , Machine learning applications in proteomics research: How the past can boost the future, PROTEOMICS, vol.1844, issue.Suppl 7), pp.353-366, 2014.
DOI : 10.1002/pmic.201300289

Y. Sun, U. Braga-neto, and E. Dougherty, A systematic model of the LC-MS proteomics pipeline, BMC Genomics, vol.13, issue.Suppl 6, 2012.
DOI : 10.1109/GENSiPS.2011.6169457

T. Fannes, E. Vandermarliere, and L. Schietgat, Predicting Tryptic Cleavage from Proteomics Data Using Decision Tree Ensembles, Journal of Proteome Research, vol.12, issue.5, pp.2253-2259, 2013.
DOI : 10.1021/pr4001114

URL : https://lirias.kuleuven.be/bitstream/123456789/395858/7/pr4001114_si_002.pdf

T. Baczek and R. Kaliszan, Predictions of peptides' retention times in reversed-phase liquid chromatography as a new supportive tool to improve protein identification in proteomics, PROTEOMICS, vol.23, issue.4, pp.835-847, 2009.
DOI : 10.1002/pmic.200800544

J. Meek, Prediction of peptide retention times in high-pressure liquid chromatography on the basis of amino acid composition, Proceedings of the National Academy of Sciences, vol.77, issue.3, pp.1632-1636, 1980.
DOI : 10.1073/pnas.77.3.1632

C. Mant and R. Hodges, Context-dependent effects on the hydrophilicity/hydrophobicity of side-chains during reversed-phase high-performance liquid chromatography: Implications for prediction of peptide retention behaviour, Journal of Chromatography A, vol.1125, issue.2, pp.211-219, 2006.
DOI : 10.1016/j.chroma.2006.05.063

A. Klammer, X. Yi, M. Maccoss, and W. Noble, Peptide Retention Time Prediction Yields Improved Tandem Mass Spectrum Identification for Diverse Chromatography Conditions, Research in Computational Molecular Biology, vol.4453, pp.459-472, 2009.
DOI : 10.1007/978-3-540-71681-5_32

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

V. Spicer, A. Yamchuk, and J. Cortens, Sequence-Specific Retention Calculator. A Family of Peptide Retention Time Prediction Algorithms in Reversed-Phase HPLC:?? Applicability to Various Chromatographic Conditions and Columns, Analytical Chemistry, vol.79, issue.22, pp.8762-8768, 2007.
DOI : 10.1021/ac071474k

L. Moruz, D. Tomazela, and L. Ka¦êll, Training, Selection, and Robust Calibration of Retention Time Models for Targeted Proteomics, Journal of Proteome Research, vol.9, issue.10, pp.5209-5216, 2010.
DOI : 10.1021/pr1005058

L. Moruz, A. Staes, and J. Foster, Chromatographic retention time prediction for posttranslationally modified peptides, PROTEOMICS, vol.5, issue.8, pp.1151-1159, 2012.
DOI : 10.1002/pmic.201100386

S. Parker, H. Rost, and G. Rosenberger, Identification of a Set of Conserved Eukaryotic Internal Retention Time Standards for Data-independent Acquisition Mass Spectrometry, Molecular & Cellular Proteomics, vol.14, issue.10, pp.2800-2813, 2015.
DOI : 10.1074/mcp.O114.042267

Y. Li, R. Arnold, and H. Tang, The Importance of Peptide Detectability for Protein Identification, Quantification, and Experiment Design in MS/MS Proteomics, Journal of Proteome Research, vol.9, issue.12
DOI : 10.1021/pr1005586

D. Wang, S. Dasari, and M. Chambers, Basophile: Accurate Fragment Charge State Prediction Improves Peptide Identification Rates, Genomics, Proteomics & Bioinformatics, vol.11, issue.2, pp.86-95, 2013.
DOI : 10.1016/j.gpb.2012.11.004

URL : http://doi.org/10.1016/j.gpb.2012.11.004

Z. Zhang, Prediction of Low-Energy Collision-Induced Dissociation Spectra of Peptides with Three or More Charges, Analytical Chemistry, vol.77, issue.19, pp.6364-6373, 2005.
DOI : 10.1021/ac050857k

R. Arnold, N. Jayasankar, and D. Aggarwal, A MACHINE LEARNING APPROACH TO PREDICTING PEPTIDE FRAGMENTATION SPECTRA, Biocomputing 2006, 2006.
DOI : 10.1142/9789812701626_0021

S. Degroeve, D. Maddelein, and L. Martens, peak intensity predictions for CID and HCD fragmentation, Nucleic Acids Research, vol.43, issue.W1, pp.326-330, 2015.
DOI : 10.1093/nar/gkv542

B. Maclean, D. Tomazela, and S. Abbatiello, Effect of Collision Energy Optimization on the Measurement of Peptides by Selected Reaction Monitoring (SRM) Mass Spectrometry, Analytical Chemistry, vol.82, issue.24, pp.10116-10124, 2010.
DOI : 10.1021/ac102179j

H. Liu, J. Zhang, H. Sun, and C. Xu, The Prediction of Peptide Charge States for Electrospray Ionization in Mass Spectrometry, Procedia Environmental Sciences, vol.8, pp.483-491, 2011.
DOI : 10.1016/j.proenv.2011.10.076

H. Barsnes and L. Martens, Crowdsourcing in proteomics: public resources lead to better experiments, Amino Acids, vol.11, issue.6, pp.1129-1137, 2013.
DOI : 10.1007/s00726-012-1455-z

M. Vaudel, K. Verheggen, and A. Csordas, Exploring the potential of public proteomics data, PROTEOMICS, vol.2013, issue.Database issue, pp.214-225, 2016.
DOI : 10.1002/pmic.201500295

Y. Perez-riverol, R. Wang, and H. Hermjakob, Open source libraries and frameworks for mass spectrometry based proteomics: A developer's perspective, Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics, vol.1844, issue.1, pp.63-76, 2014.
DOI : 10.1016/j.bbapap.2013.02.032

URL : http://doi.org/10.1016/j.bbapap.2013.02.032

L. Martens, H. Hermjakob, and P. Jones, The proteomics identifications database, Proteomics, vol.13, pp.3537-3545, 2005.

J. Vizcano and A. Csordas, The Proteomics Identifications (PRIDE) database and associated tools: status in 2013, Nucleic Acids Research, vol.41, issue.D1, pp.1063-1069, 2013.
DOI : 10.1093/nar/gks1262

R. Craig, J. Cortens, and R. Beavis, Open Source System for Analyzing, Validating, and Storing Protein Identification Data, Journal of Proteome Research, vol.3, issue.6, pp.1234-1242, 2004.
DOI : 10.1021/pr049882h

E. Deutsch, H. Lam, and R. Aebersold, PeptideAtlas: a resource for target selection for emerging targeted proteomics workflows, EMBO reports, vol.18, issue.5, pp.429-434, 2008.
DOI : 10.1101/gr.5646507

T. Farrah, E. Deutsch, and R. Kreisberg, PASSEL: The PeptideAtlas SRMexperiment library, PROTEOMICS, vol.22, issue.Dec. 12, pp.1170-1175, 2012.
DOI : 10.1002/pmic.201100515

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3832291

S. Mathivanan, M. Ahmed, and N. Ahn, Human Proteinpedia enables sharing of human protein data, Nature Biotechnology, vol.23, issue.2, pp.164-167, 2008.
DOI : 10.1038/nbt0208-164

F. Gnad, M. Oroshi, and E. Birney, MAPU 2.0: high-accuracy proteomes mapped to genomes, Nucleic Acids Research, vol.37, issue.Database, pp.902-906, 2009.
DOI : 10.1093/nar/gkn773

M. Riffle, L. Malmstram, and T. Davis, The Yeast Resource Center Public Data Repository, Nucleic Acids Research, vol.33, issue.Database issue, pp.378-382, 2005.
DOI : 10.1093/nar/gki073

URL : http://doi.org/10.1093/nar/gki073

. Vizcanoja, M. Mueller, and H. Hermjakob, Charting online OMICS resources: A navigational chart for clinical researchers, Prot.Clin.Appl, vol.1, pp.18-29, 2009.

J. Vizcano, J. Foster, and L. Martens, Proteomics data repositories: Providing a safe haven for your data and acting as a springboard for further research, Journal of Proteomics, vol.73, issue.11, pp.2136-2146, 2010.
DOI : 10.1016/j.jprot.2010.06.008

H. Lam and R. Aebersold, Building and searching tandem mass (MS/MS) spectral libraries for peptide identification in proteomics, Methods, vol.54, issue.4, pp.424-431, 2011.
DOI : 10.1016/j.ymeth.2011.01.007

M. Ashburner, C. Ball, and J. Blake, Gene ontology: tool for the unification of biology. The Gene Ontology Consortium, Nat.Genet, vol.1, pp.25-29, 2000.

M. Kanehisa and S. Goto, KEGG: Kyoto Encyclopedia of Genes and Genomes, Nucleic Acids Research, vol.28, issue.1, pp.27-30, 2000.
DOI : 10.1093/nar/28.1.27

L. Matthews, G. Gopinath, and M. Gillespie, Reactome knowledgebase of human biological pathways and processes, Database issue, pp.619-622, 2009.
DOI : 10.1093/nar/gkn863

D. Smedley, S. Haider, and B. Ballester, BioMart ??? biological queries made easy, BMC Genomics, vol.10, issue.1, 2009.
DOI : 10.1186/1471-2164-10-22

URL : http://doi.org/10.1186/1471-2164-10-22

M. Macleod, M. Lawson, and A. Kyriakopoulou, Risk of Bias in Reports of In Vivo Research: A Focus for Improvement, PLOS Biology, vol.350, issue.2, p.1002273, 2015.
DOI : 10.1371/journal.pbio.1002273.s007

D. Tabb, Quality assessment for clinical proteomics, Clinical Biochemistry, vol.46, issue.6, pp.411-420, 2013.
DOI : 10.1016/j.clinbiochem.2012.12.003

URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3602213