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pmid: 21901605
Affinity proteomics, mainly represented by antibody microarrays, has in recent years been established as a powerful tool for high-throughput (disease) proteomics. The technology can be used to generate detailed protein expression profiles, or protein maps, of focused set of proteins in crude proteomes and potentially even high-resolution portraits of entire proteomes. The technology provides unique opportunities, for example biomarker discovery, disease diagnostics, patient stratification and monitoring of disease, and taking the next steps toward personalized medicine. However, the process of designing high-performing, high-density antibody micro- and nanoarrays has proven to be challenging, requiring truly cross-disciplinary efforts to be adopted. In this mini-review, we address one of these key technological issues, namely, the choice of probe format, and focus on the use of recombinant antibodies vs. polyclonal and monoclonal antibodies for the generation of antibody arrays.
Models, Molecular, Proteomics, Gene Expression Profiling, Antibody Affinity, Protein Array Analysis, Protein Engineering, Antibodies, Recombinant Proteins, Humans, Precision Medicine
Models, Molecular, Proteomics, Gene Expression Profiling, Antibody Affinity, Protein Array Analysis, Protein Engineering, Antibodies, Recombinant Proteins, Humans, Precision Medicine
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 35 | |
popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |