Super-SILAC mix for quantitative proteomics of human tumor tissue.
about
Integrative "omics"-approach discovers dynamic and regulatory features of bacterial stress responsesProteomics of mouse BRCA1-deficient mammary tumors identifies DNA repair proteins with potential diagnostic and prognostic value in human breast cancerNon-muscle myosin II in disease: mechanisms and therapeutic opportunitiesAnalysis of oncogenic signaling induced by tyrosine kinases in tumors by SILAC-based quantitative proteomic approachMass spectrometry for translational proteomics: progress and clinical implicationsRecent advances in quantitative neuroproteomicsA Proteogenomic Approach to Understanding MYC Function in Metastatic Medulloblastoma TumorsCardiovascular proteomics in the era of big data: experimental and computational advancesMass spectrometry-based approaches to targeted quantitative proteomics in cardiovascular diseaseProteomic discovery of host kinase signaling in bacterial infectionsCurrent strategies and findings in clinically relevant post-translational modification-specific proteomicsProteomic response to 5,6-dimethylxanthenone 4-acetic acid (DMXAA, vadimezan) in human non-small cell lung cancer A549 cells determined by the stable-isotope labeling by amino acids in cell culture (SILAC) approachVersatile, sensitive liquid chromatography mass spectrometry - Implementation of 10 μm OT columns suitable for small molecules, peptides and proteinsSPECHT - single-stage phosphopeptide enrichment and stable-isotope chemical tagging: quantitative phosphoproteomics of insulin action in muscle.Chemical analysis of single cells.Phosphosignature predicts dasatinib response in non-small cell lung cancer.Accurate LC peak boundary detection for ¹⁶O/¹⁸O labeled LC-MS dataSystematic assessment of survey scan and MS2-based abundance strategies for label-free quantitative proteomics using high-resolution MS data.EBprot: Statistical analysis of labeling-based quantitative proteomics data.Global Cell Proteome Profiling, Phospho-signaling and Quantitative Proteomics for Identification of New Biomarkers in Acute Myeloid Leukemia PatientsQuantitative proteomics of synaptic and nonsynaptic mitochondria: insights for synaptic mitochondrial vulnerability.Aging synaptic mitochondria exhibit dynamic proteomic changes while maintaining bioenergetic functionAnalysis of N-glycoproteins using genomic N-glycosite prediction.A genetic engineering solution to the "arginine conversion problem" in stable isotope labeling by amino acids in cell culture (SILAC).SCFIA: a statistical corresponding feature identification algorithm for LC/MS.Mass spectrometry-based proteomics in cell biologyProcessing methods for signal suppression of FTMS data.PeakLink: a new peptide peak linking method in LC-MS/MS using wavelet and SVM.Comparing SILAC- and stable isotope dimethyl-labeling approaches for quantitative proteomicsMass spectrometry-based proteomics: existing capabilities and future directionsInvestigation of receptor interacting protein (RIP3)-dependent protein phosphorylation by quantitative phosphoproteomicsDisclosure of selective advantages in the "modern" sublineage of the Mycobacterium tuberculosis Beijing genotype family by quantitative proteomicsDiminished superoxide generation is associated with respiratory chain dysfunction and changes in the mitochondrial proteome of sensory neurons from diabetic ratsAccurate quantification of more than 4000 mouse tissue proteins reveals minimal proteome changes during aging.A Cell-Surface Membrane Protein Signature for Glioblastoma.Protein analysis by shotgun/bottom-up proteomicsAnalytical challenges translating mass spectrometry-based phosphoproteomics from discovery to clinical applications.Analytical validation considerations of multiplex mass-spectrometry-based proteomic platforms for measuring protein biomarkers.UNiquant, a program for quantitative proteomics analysis using stable isotope labeling.The path to clinical proteomics research: integration of proteomics, genomics, clinical laboratory and regulatory science.
P2860
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P2860
Super-SILAC mix for quantitative proteomics of human tumor tissue.
description
2010 nî lūn-bûn
@nan
2010年の論文
@ja
2010年論文
@yue
2010年論文
@zh-hant
2010年論文
@zh-hk
2010年論文
@zh-mo
2010年論文
@zh-tw
2010年论文
@wuu
2010年论文
@zh
2010年论文
@zh-cn
name
Super-SILAC mix for quantitative proteomics of human tumor tissue.
@en
Super-SILAC mix for quantitative proteomics of human tumor tissue.
@nl
type
label
Super-SILAC mix for quantitative proteomics of human tumor tissue.
@en
Super-SILAC mix for quantitative proteomics of human tumor tissue.
@nl
prefLabel
Super-SILAC mix for quantitative proteomics of human tumor tissue.
@en
Super-SILAC mix for quantitative proteomics of human tumor tissue.
@nl
P2093
P356
P1433
P1476
Super-SILAC mix for quantitative proteomics of human tumor tissue.
@en
P2093
Jacek R Wisniewski
Juergen Cox
Matthias Mann
Pawel Ostasiewicz
Tamar Geiger
P2888
P304
P356
10.1038/NMETH.1446
P577
2010-04-04T00:00:00Z