Protein-protein interaction- a Bioinformatics approach to discover novel biomarkers of diseases
Bibliographic record
Abstract
Models of Human Diseases and Protein-protein interaction of diseases projects were initiated by Dr. Lorelei Silverman and Dr. Rosalind Silverman at University of Toronto, Canada and now continued by Medical Education Advising. To our knowledge it is the biggest database for models of human diseases and PPI spiders for biomarkers in the world. The aim of the project was to develop protein-protein interaction spiders for all diseases using a combined proteomics and bioinformatics approach. Understanding these interactions is important for design of corrective therapeutic strategies. Targeting several pathways simultaneously, rather than a single aspect of the complex diseases is crucial for advancement of novel treatmentsSome projects in learning and memory, hypoxia, neuromuscular, cardiovascular, cancer diseases, etc are more advanced and we already published articles based on this approach in papers like Cell, Journal of Neuroscience, American Journal of Pathology, Cytoskeleton, Neuroscience, Journal of Neurochemistry,etc . Others are just initiated or in progress.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.014 |
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".