Model-Agnostic Interpretation of Cancer Classification with Multi-Platform Genomic Data
Bibliographic record
Abstract
Machine learning models are often criticised for being black-boxes. Recent work in this field has aimed to address this criticism by developing methods to explain the underlying behaviour of machine learning models. These explanations are designed to help the end-user interpret how the models input features are used to make a prediction. Here, we present an extension to one such method, referred to as local interpretable model-agnostic explanations, to interpret multimodal tumor type classification from multi-platform genomic data. We propose a framework for transparent biomedical machine learning by leveraging interpretable dimensionality reduction to facilitate gene-wise explanations for the model behaviour. Using RNA-seq expression and single nucleotide variation (SNV) data from eight cancer types, our experimental results uncovered the models use of clinically relevant genes for cancer cell stratification. We demonstrate that model-agnostic explanations can provide valuable information to a clinician or scientist when predictive ability and interpretability are of absolute importance.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".