A perspective on the use of iTRAQTM reagent technology for protein complex and profiling studies
Bibliographic record
Abstract
Proteomic research includes the characterization of protein mixtures in order to understand complex biological systems and determine relationships between proteins, their function, and protein-protein interactions. Often the goal of such research is to monitor changes of proteins in perturbed systems, a type of study referred to as differential expression analysis. To perform these studies requires the ability to execute some type of differential comparison of a given protein state in reference to some type of a control. The iTRAQ reagents are a set of isobaric reagents which are amine specific and allow for the identification and quantitation of up to four different samples simultaneously. The amine specificity of these reagents makes most peptides in a sample amenable to this labeling strategy with no loss of information from samples involving post-translational modifications, such as the scrutiny of signal transduction pathways that often involve phosphorylation phenomena. In addition, the multiplexing capacity of these reagents allows for information replication within certain LC-MS/MS experimental regimes, providing additional statistical validation within any given experiment. The results presented herein demonstrate a few examples of the wide variety of quantitative information that can be realized when undertaking such experimental approaches. These include temporal analysis of drug-induced-protein expression, discovery and elucidation of disease markers, and protein-protein interactions in multi-protein complexes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".