Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics.
about
MUMAL: multivariate analysis in shotgun proteomics using machine learning techniquesOpenMS - an open-source software framework for mass spectrometryLC-MSsim--a simulation software for liquid chromatography mass spectrometry dataIn silico design of targeted SRM-based experiments.Combination of Edman degradation of peptides with liquid chromatography/mass spectrometry workflow for peptide identification in bottom-up proteomics.Optimal precursor ion selection for LC-MALDI MS/MS.A computational tool to detect and avoid redundancy in selected reaction monitoring.MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithmLocus-specific Retention Predictor (LsRP): A Peptide Retention Time Predictor Developed for Precision Proteomics.Proteomic reactors and their applications in biology.Machine learning applications in proteomics research: how the past can boost the future.Peptide retention time prediction.Predictive chromatography of peptides and proteins as a complementary tool for proteomics.Highly multiplexed targeted proteomics using precise control of peptide retention time.Properties of isotope patterns and their utility for peptide identification in large-scale proteomic experiments.Chromatographic retention time prediction for posttranslationally modified peptides.Peptide retention prediction in reversed-phase chromatography: proteomic applications.Peptide Purification by Reversed-Phase ChromatographyVisualization and application of amino acid retention coefficients obtained from modeling of peptide retention
P2860
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P2860
Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics.
description
2007 nî lūn-bûn
@nan
2007 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած
@hyw
2007 թվականի նոյեմբերին հրատարակված գիտական հոդված
@hy
2007年の論文
@ja
2007年論文
@yue
2007年論文
@zh-hant
2007年論文
@zh-hk
2007年論文
@zh-mo
2007年論文
@zh-tw
2007年论文
@wuu
name
Statistical learning of peptid ...... for computational proteomics.
@ast
Statistical learning of peptid ...... for computational proteomics.
@en
Statistical learning of peptid ...... for computational proteomics.
@nl
type
label
Statistical learning of peptid ...... for computational proteomics.
@ast
Statistical learning of peptid ...... for computational proteomics.
@en
Statistical learning of peptid ...... for computational proteomics.
@nl
prefLabel
Statistical learning of peptid ...... for computational proteomics.
@ast
Statistical learning of peptid ...... for computational proteomics.
@en
Statistical learning of peptid ...... for computational proteomics.
@nl
P2860
P50
P356
P1433
P1476
Statistical learning of peptid ...... for computational proteomics.
@en
P2093
Andreas Leinenbach
P2860
P2888
P356
10.1186/1471-2105-8-468
P577
2007-11-30T00:00:00Z
P5875
P6179
1035978609