Nine steps to proteomic wisdom: A practical guide to using protein‐protein interaction networks and molecular pathways as a framework for interpreting disease proteomic profiles
Bibliographic record
Abstract
A major aim of proteomic profiling of disease is to uncover the mechanistic basis of a given pathology. High-throughput experimental techniques continue to advance rapidly, but are still plagued by high rates of false negatives, false positives, and other spurious findings. By reducing a disease profile to a subset of differentially expressed proteins and determining functional over-representation, one can often make a reasonable first-pass assessment as to what might be happening in disease. Integrating mRNA expression patterns together with prior knowledge of protein-protein interaction networks and biological pathway information goes a step further, providing clues into the core processes that are aberrant in the disease state, and indicating which cellular functions are activated or repressed as a maladaptive pathophysiological response. This multi-step framework allows one to hypothesize as to possible cause and effect of pathology, and highlights potentially instructive pathways or sub-networks for subsequent experimental validation. Indeed, efficiently exploiting data regarding the myriad of physical and genetic interactions among expressed gene products, in parallel with the systematic sampling of genetic variation among diverse human populations, promises to revolutionize our current understanding of disease action at a deeper molecular level.
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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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.007 | 0.021 |
| Insufficient payload (model declined to judge) | 0.013 | 0.015 |
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".