A Large-scale and Extensible Platform for Precision Medicine Research
Bibliographic record
Abstract
The massive adoption of high-throughput genomics, deep sequencing technologies and big data technologies have made possible the era of precision medicine. However, the volume of data and its complexity remain important challenges for precision medicine research, hindering development in this field. The literature on precision medicine research describes a few platforms to support specific types of studies, but none of these offer researchers the level of customization required to meet their specific needs [1]. Methods: We propose to design and develop a platform able to import and integrate a very large volume of genetics, clinical, demographical and environmental data in a cloud computing infrastructure. In our previous publication, we presented an approach that can customize existing data models to fit any precision medicine research data requirement [1] and the requirement for future large-scale precision medicine platforms, in terms of data extensibility and the scalability of processing on demand. We also proposed a framework to meet the specific requirement of any precision medicine research [2]. In this paper, we describe how this new framework was implemented and trialed by the precision medicine researchers at the Centre Hospitalier Universitaire de l'Université de Montréal (CHUM). Results: The data analysis simulations showed that the random forest algorithm presents better accuracy results. We obtained an F1-Score of 72% for random forest, 69% using linear regression and 62% using the neural network algorithm. Conclusion: The results suggest that the proposed precision medicine data analysis platform allows researchers to configure, prepare the analysis environment and customize the platform data model to their specific research in very optimal delays, at very low cost and with minimal technical skills.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".