A Systematic Approach for Analysis of Peptide Array Kinome Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The central roles of kinases in cellular processes and diseases make them highly attractive as indicators of biological responses and as therapeutic targets. Peptide arrays are emerging as an important means of characterizing kinome activity. Currently, the computational tools used to perform high-throughput kinome analyses are not specifically tailored to the nature of the data, which hinders extraction of biological information and overall progress in the field. We have developed a method for kinome analysis, which is implemented as a software pipeline in the R environment. Components and parameters were chosen to address the technical and biological characteristics of kinome microarrays. We performed comparative analysis of kinome data sets that corresponded to stimulation of immune cells with ligands of well-defined signaling pathways: bovine monocytes treated with interferon-γ (IFN-γ), CpG-containing nucleotides, or lipopolysaccharide (LPS). The data sets for each of the treatments were analyzed with our methodology as well as with three other commonly used approaches. The methods were evaluated on the basis of statistical confidence of calculated values with respect to technical and biological variability, and the statistical confidence (P values) by which the known signaling pathways could be independently identified by the pathway analysis of InnateDB (a Web-based resource for innate immunity interactions and pathways). By considering the particular attributes of kinome data, we found that our approach identified more of the peptides involved in the pathways than did the other compared methods and that it did so at a much higher degree of statistical confidence.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it