Real-time molecular classification of leukemias
Bibliographic record
Abstract
Abstract Gene expression profiling provides a detailed molecular snapshot of cellular phenotypes that can be used to compare different biological conditions. Nanopore sequencing technology can generate high-resolution transcriptomic data in real-time and at low cost, which heralds new opportunities for molecular medicine. In this study, we demonstrate the clinical utility of real-time transcriptomic profiling by processing RNA sequencing data from childhood acute lymphoblastic leukemia (ALL) patients on-the-fly with a trained neural network classifier. This strategy successfully distinguished 11/12 representative ALL molecular subtypes and one non-leukemia control in as little as 5 minutes of sequencing on a MinION sequencer or in less than 1 hour on disposable, low cost Flongle flow cells. Our findings suggest that real-time transcriptomics constitutes a drastically efficient solution for the molecular diagnosis of ALL and other diseases, where conventional clinical workflows require days if not weeks to achieve similar results.
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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".