Building an Integrated Multi-Omics Database for Rare Diseases
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
Rare diseases are diverse in types and have a small number of patients with each type, but they cumulatively affect hundreds of millions of patients worldwide. Current research on rare diseases is confronted with challenges such as scattered data, inconsistent standards and difficulties in sharing. This article reviews the characteristics of the existing major rare disease databases (such as Orphanet, RD-Connect, MONDO, etc.), discusses the progress and limitations of multi-omics data integration methods, and introduces the new trend of data-driven rare disease research in the era of precision medicine. The application prospects of this database in discovering disease markers and therapeutic targets, supporting clinical decision-making and patient stratification, integrating artificial intelligence prediction models and drug reuse, etc. were explored. The contributions and main findings of this study were summarized. The potential impact of this integrated database on rare disease research and clinical translation was emphasized, and ideas for future expansion and sustainable development were proposed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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