| scholarly article | Q13442814 |
| review article | Q7318358 |
| P8978 | DBLP publication ID | journals/bmcbi/GranholmNK12 |
| P6179 | Dimensions Publication ID | 1008491559 |
| P356 | DOI | 10.1186/1471-2105-13-S16-S3 |
| P932 | PMC publication ID | 3489528 |
| P698 | PubMed publication ID | 23176259 |
| P5875 | ResearchGate publication ID | 233766437 |
| P50 | author | William S. Noble | Q29053213 |
| Lukas Käll | Q41047530 | ||
| P2093 | author name string | Viktor Granholm | |
| P2860 | cites work | Statistical significance for genomewide studies | Q24681264 |
| Probability-based protein identification by searching sequence databases using mass spectrometry data | Q27860736 | ||
| An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database | Q27861106 | ||
| A statistical model for identifying proteins by tandem mass spectrometry | Q28186251 | ||
| OLAV: towards high-throughput tandem mass spectrometry data identification | Q28202185 | ||
| Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search | Q28211385 | ||
| Protein identification false discovery rates for very large proteomics data sets generated by tandem mass spectrometry | Q28252235 | ||
| MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification | Q29547200 | ||
| Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry | Q29547311 | ||
| TANDEM: matching proteins with tandem mass spectra | Q29615238 | ||
| Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome | Q29615379 | ||
| Modeling peptide fragmentation with dynamic Bayesian networks for peptide identification | Q30490288 | ||
| Improvements to the percolator algorithm for Peptide identification from shotgun proteomics data sets | Q33433611 | ||
| Efficient marginalization to compute protein posterior probabilities from shotgun mass spectrometry data | Q33659299 | ||
| Direct maximization of protein identifications from tandem mass spectra | Q35751671 | ||
| Effects of modified digestion schemes on the identification of proteins from complex mixtures | Q36874088 | ||
| On using samples of known protein content to assess the statistical calibration of scores assigned to peptide-spectrum matches in shotgun proteomics | Q40741382 | ||
| Non-parametric estimation of posterior error probabilities associated with peptides identified by tandem mass spectrometry | Q42005545 | ||
| Rapid and accurate peptide identification from tandem mass spectra | Q43147563 | ||
| Probability-based validation of protein identifications using a modified SEQUEST algorithm | Q43884672 | ||
| Qscore: an algorithm for evaluating SEQUEST database search results | Q44727403 | ||
| Impact of ion trap tandem mass spectra variability on the identification of peptides. | Q45959769 | ||
| Semi-supervised learning for peptide identification from shotgun proteomics datasets. | Q45965072 | ||
| Probability-based pattern recognition and statistical framework for randomization: modeling tandem mass spectrum/peptide sequence false match frequencies | Q48400535 | ||
| Estimating the statistical significance of peptide identifications from shotgun proteomics experiments. | Q51034842 | ||
| Semisupervised model-based validation of peptide identifications in mass spectrometry-based proteomics. | Q51897865 | ||
| Quality assessments of peptide-spectrum matches in shotgun proteomics | Q57909981 | ||
| A meeting with Enrico Fermi | Q59069242 | ||
| P921 | main subject | algorithm | Q8366 |
| machine learning | Q2539 | ||
| Shotgun proteomics | Q7502514 | ||
| P304 | page(s) | S3 | |
| P577 | publication date | 2012-11-05 | |
| P1433 | published in | BMC Bioinformatics | Q4835939 |
| P1476 | title | A cross-validation scheme for machine learning algorithms in shotgun proteomics | |
| P478 | volume | 13 Suppl 16 |
| Q88242197 | A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics |
| Q62843842 | Averaging Strategy To Reduce Variability in Target-Decoy Estimates of False Discovery Rate |
| Q37684591 | Fast and accurate database searches with MS-GF+Percolator |
| Q36254939 | MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithm |
| Q38170020 | Machine learning applications in proteomics research: how the past can boost the future |
| Q30994995 | Optimization of Search Engines and Postprocessing Approaches to Maximize Peptide and Protein Identification for High-Resolution Mass Data |
| Q28553325 | Performance Investigation of Proteomic Identification by HCD/CID Fragmentations in Combination with High/Low-Resolution Detectors on a Tribrid, High-Field Orbitrap Instrument |
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