From big data to personalized medicine: bioinformatics perspectives and challenges
Bibliographic record
Abstract
The rapid accumulation of various types of -omics data is opening new vistas for the development of novel therapeutic approaches grounded in big data. Particularly, personalized medicine stands out as one of the most promising goals. However, it faces significant logical and mathematical challenges. These include the ecological fallacy, which refers to making individual predictions based on average population data, and the Pareto principle, which suggests that approximately 80% of outcomes are driven by 20% of inputs. These raise the possibility of a dramatic discrepancy between our expectations for big data–based personalized medicine and its actual effectiveness. What are the practical issues our community must address to bridge the gap between the promise of big data and the reality of personalized medicine?
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.
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 | 0.061 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.020 | 0.044 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.017 | 0.031 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".