Expansion of reactome functional interaction network to allow exposure of knowledge space of understudied proteins in the context of biological pathways
Bibliographic record
Abstract
Currently one of the major overarching questions in research is “how to match drug response with omics?”. Even in the era of super computation and massive data flow, a small fraction of the human genome is still understudied. Designing clinically actionable therapeutics targeting proteins encoded by these understudied genes requires an expansion of knowledge space. Reactome is the most comprehensive, open-source biological pathway knowledgebase, widely used for pathway analysis and visualization. As part of the Cutting Edge Informatics Tools program of the NIH Illuminating the Druggable Genome (IDG) Consortium, we are expanding the Reactome Functional Interaction Network to provide a pathway and network knowledge space for these understudied proteins. We have collected more than 100 highly reliable protein pairwise relationship data sources, including tissue/cancer-specific gene coexpressions from GTEx and TCGA, gene similarities from Harmonizome, and protein-protein interactions from StringDB, BioGrid, and BioPlex. We are developing a machine learning approach to integrate these data sources to predict functional relationships between understudied proteins and Reactome annotated proteins. Placing these understudied proteins in the context of Reactome pathways will facilitate generation of hypotheses for new potential targetable proteins. We have made the data accessible and navigable via our new Reactome IDG portal, idg.reactome.org.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".