Predicting ionizing radiation exposure using biochemically-inspired genomic machine learning
Bibliographic record
Abstract
<ns3:p><ns3:bold>Background:</ns3:bold> Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches.</ns3:p><ns3:p> <ns3:bold>Methods:</ns3:bold> Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets.</ns3:p><ns3:p> <ns3:bold>Results:</ns3:bold> The best human signatures we derived exhibit k-fold validation accuracies of up to 98% (<ns3:italic>DDB2</ns3:italic>, <ns3:italic> PRKDC</ns3:italic>,<ns3:italic> TPP2</ns3:italic>, <ns3:italic>PTPRE</ns3:italic>, and<ns3:italic> GADD45A</ns3:italic>) when validated over 209 samples and traditional validation accuracies of up to 92% (<ns3:italic>DDB2</ns3:italic>, <ns3:italic> CD8A</ns3:italic>, <ns3:italic> TALDO1</ns3:italic>, <ns3:italic> PCNA</ns3:italic>, <ns3:italic> EIF4G2</ns3:italic>, <ns3:italic> LCN2</ns3:italic>, <ns3:italic> CDKN1A</ns3:italic>, <ns3:italic> PRKCH</ns3:italic>, <ns3:italic> ENO1</ns3:italic>, and<ns3:italic> PPM1D</ns3:italic>) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures.</ns3:p><ns3:p> <ns3:bold>Conclusions:</ns3:bold> Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.</ns3:p>
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 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 | 0.001 | 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".