Eight quick tips for biologically and medically informed machine learning
Bibliographic record
Abstract
Machine learning has become a powerful tool for computational analysis in the biomedical sciences, with its effectiveness significantly enhanced by integrating domain-specific knowledge. This integration has give rise to informed machine learning, in contrast to studies that lack domain knowledge and treat all variables equally (uninformed machine learning). While the application of informed machine learning to bioinformatics and health informatics datasets has become more seamless, the likelihood of errors has also increased. To address this drawback, we present eight guidelines outlining best practices for employing informed machine learning methods in biomedical sciences. These quick tips offer recommendations on various aspects of informed machine learning analysis, aiming to assist researchers in generating more robust, explainable, and dependable results. Even if we originally crafted these eight simple suggestions for novices, we believe they are deemed relevant for expert computational researchers as well.
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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 | 0.036 | 0.190 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.023 | 0.017 |
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".