Towards a Biological Evaluation Framework for Oversampling (BEFO) gene expression data
Bibliographic record
Abstract
Machine learning (ML) techniques are progressively being used in biomedical research to improve diagnostic and prognostic accuracy when used in conjunction with a clinician as a decision support system. However, many datasets used in biomedical research often suffer from severe class imbalance due to small population sizes, which causes machine learning models to become biased to majority class samples. Current oversampling methods primarily focus on balancing datasets without adequately validating the biological relevance of synthetic data, risking the clinical applicability of downstream model predictions. To address these shortcomings, we propose the Biological Evaluation Framework for Oversampling (BEFO) designed to ensure that synthetic gene expression samples accurately reflect the biological patterns present in original datasets. This innovation not only mitigates bias but enhances the trustworthiness of predictive models in clinical scenarios. We have developed a ranking method for synthetic samples based on this and evaluated each sample's inclusion based on its rank. This ranking method calculates the WGCNA gene co-expression clusters on the original dataset. Several random forests are constructed to assess the alignment of each synthetic sample to each cluster. Only synthetic samples more important than real samples are included in a study. The experimental results demonstrate that our proposed ML oversampling framework can improve the biological feasibility of oversampled datasets by an average of 11%, leading to improved classification performance by an average of 9% when compared against five state-of-the-art (SOTA) oversampling methods and ten classification algorithms across six real world gene expressions datasets. Thereby establishing a new standard for synthetic data evaluation in biomedical ML applications.
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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 | 0.031 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".