Large-Scale Protein-Protein Interaction Detection Approaches: Past, Present and Future
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
Protein-protein interaction elucidation is of immense importance to biology, medicine, and related fields. It is now realized that various diseases such as different types of cancers, Alzheimer's disease, etc, require an integrated view of protein interaction networks. To aid in deciphering these networks, a number of methods have been developed including yeast two-hybrid analysis, tandem affinity purification tagging, as well as protein microarray technologies. In this article, we discuss some of the most important trends and technologies of the past, reflect on their present, and explore some exciting future directions in the field of large-scale protein-protein interaction detection. We argue that the future of the protein interaction elucidation field lies in the development of novel and/or improved high-throughput techniques that generate reproducible and most importantly, quantitative data.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 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