Mapping the Disease Protein Interactome: Toward a Molecular Medicine GPS to Accelerate Drug and Biomarker Discovery
Bibliographic record
Abstract
Genomic approaches such as genome-wide association studies (GWAS), disease genome sequencing projects, and genome-wide expression profiling analyses, in conjunction with classical genetic approaches, can identify human genes that are altered in disease, thereby suggesting a role for the encoded protein (or RNA) in the establishment and/or progression of the disease. However, many technical difficulties challenge our ability to validate the role of these disease-associated genes and gene products. Moreover, many identified genes contain open reading frames (ORFs) that have yet to be annotated, that is, the function (or activity) of the encoded protein is unknown. As a result, translating the genomic information available in public databases into useful tools for understanding and curing disease is a very slow and inefficient process. To overcome these difficulties, we have developed a technology platform, termed the "molecular medicine GPS" (mm-GPS), which is aimed at defining high-quality maps of interaction networks involving disease proteins. These maps are used to identify network dysfunctions in disease cells or models and to develop molecular tools such as RNA interference (RNAi) and small-molecule inhibitors to further characterize the molecular basis of disease. In this article, I review our progress in producing high-quality maps of human protein interaction networks, and I describe how we used this information to identify new factors and pathways that regulate the RNA polymerase II transcription machinery. I also describe how we utilize the mm-GPS platform to guide more efficient efforts leading from disease-associated genes to protein interaction networks to small-molecule inhibitors, and consequently, to accelerate drug and biomarker discovery.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".