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
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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