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Label-free quantitative proteomics and SAINT analysis enable interactome mapping for the human Ser/Thr protein phosphatase 5The functional interactome landscape of the human histone deacetylase familyA PP2A phosphatase high density interaction network identifies a novel striatin-interacting phosphatase and kinase complex linked to the cerebral cavernous malformation 3 (CCM3) proteinThe PeptideAtlas projectQuantitative mass spectrometry reveals a role for the GTPase Rho1p in actin organization on the peroxisome membrane.A global protein kinase and phosphatase interaction network in yeast.The transcription elongation factor TFIIS is a component of RNA polymerase II preinitiation complexes.The yeast Sks1p kinase signaling network regulates pseudohyphal growth and glucose responseIntegrated phosphoproteomics analysis of a signaling network governing nutrient response and peroxisome induction.A statistical model for identifying proteins by tandem mass spectrometryEmpirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database searchStatistical validation of peptide identifications in large-scale proteomics using the target-decoy database search strategy and flexible mixture modelingHands-on workshops as an effective means of learning advanced technologies including genomics, proteomics and bioinformaticsMetabolites of purine nucleoside phosphorylase (NP) in serum have the potential to delineate pancreatic adenocarcinomaProteogenomics: concepts, applications and computational strategiesToward more transparent and reproducible omics studies through a common metadata checklist and data publicationsComputational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experimentsRecommendations from the 2008 International Summit on Proteomics Data Release and Sharing Policy: the Amsterdam principles"Topological significance" analysis of gene expression and proteomic profiles from prostate cancer cells reveals key mechanisms of androgen responseA guided tour of the Trans-Proteomic PipelineSAINT-MS1: protein-protein interaction scoring using label-free intensity data in affinity purification-mass spectrometry experiments.Analyzing protein-protein interactions from affinity purification-mass spectrometry data with SAINTUsing ProHits to store, annotate, and analyze affinity purification-mass spectrometry (AP-MS) dataGSK3β controls epithelial-mesenchymal transition and tumor metastasis by CHIP-mediated degradation of SlugThe CRAPome: a contaminant repository for affinity purification-mass spectrometry data.Reconstructing targetable pathways in lung cancer by integrating diverse omics dataComprehensive analysis of proteins of pH fractionated samples using monolithic LC/MS/MS, intact MW measurement and MALDI-QIT-TOF MSLuciPHOr2: site localization of generic post-translational modifications from tandem mass spectrometry data.DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomicsInterpretation of shotgun proteomic data: the protein inference problem.Do we want our data raw? Including binary mass spectrometry data in public proteomics data repositories.Investigating MS2/MS3 matching statistics: a model for coupling consecutive stage mass spectrometry data for increased peptide identification confidence.Analysis and validation of proteomic data generated by tandem mass spectrometry.Significance analysis of spectral count data in label-free shotgun proteomicsDynamic spectrum quality assessment and iterative computational analysis of shotgun proteomic data: toward more efficient identification of post-translational modifications, sequence polymorphisms, and novel peptides.Hierarchical hidden Markov model with application to joint analysis of ChIP-chip and ChIP-seq data.Proteomic interrogation of androgen action in prostate cancer cells reveals roles of aminoacyl tRNA synthetasesComputational analysis of unassigned high-quality MS/MS spectra in proteomic data sets.Analysis of protein complexes through model-based biclustering of label-free quantitative AP-MS data.Quantitative proteomic profiling of prostate cancer reveals a role for miR-128 in prostate cancer.
P50
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P50
description
hulumtues
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researcher
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հետազոտող
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Alexey I Nesvizhskii
@ast
Alexey I Nesvizhskii
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Alexey I Nesvizhskii
@nl
Alexey I Nesvizhskii
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Alexey I. Nesvizhskii
@en
type
label
Alexey I Nesvizhskii
@ast
Alexey I Nesvizhskii
@es
Alexey I Nesvizhskii
@nl
Alexey I Nesvizhskii
@sl
Alexey I. Nesvizhskii
@en
prefLabel
Alexey I Nesvizhskii
@ast
Alexey I Nesvizhskii
@es
Alexey I Nesvizhskii
@nl
Alexey I Nesvizhskii
@sl
Alexey I. Nesvizhskii
@en
P1053
A-5410-2012
P106
P21
P2456
P2798
P31
P3829
P496
0000-0002-2806-7819