Discovering Monogenic Causes of Multi-Diseases by Mining Electronic Medical Records and Genetics Repositories
Bibliographic record
Abstract
We present a method called SMDG (Single Multi-Disease Genes) for systematic discovery of monogenic causes of multi-diseases. Multi-disease conditions, quite common in older populations, are difficult to treat due to missing their precise medical guidelines and need for attention of multiple health care providers. Finding monogenic causes of these diseases would enable introducing new therapeutic approaches, focused on the remediation of mutations of single genes. SMDG is based on the hierarchical divisive clustering of electronic medical records (EMR) that include genetic data, and on the analysis of the public gene-to-disease and gene-to-gene repositories. The method was tested on the database of the Harvard Personal Genome Project (PGP), the gene-to-disease repository DisGeNET and the gene-to-gene interactions repository BioGRID. It identified possible new monogenic causes of selected multi-diseases, which were confirmed as valid hypotheses by examining related research papers.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.028 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".