upsAI: A high-accuracy machine learning classifier for predicting <i>Plasmodium falciparum var</i> gene upstream groups
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Plasmodium falciparum erythrocyte membrane protein 1 ( Pf EMP1), encoded by the hypervariable var gene family, is central to malaria pathogenesis, influencing both disease severity and immune evasion. Classifying var genes into upstream groups (upsA, upsB, upsC, upsE) is important for understanding parasite biology and clinical outcomes, but remains challenging, especially with partial sequences, such as the DBLα tag or RNA-Seq assemblies. We developed upsAI, a machine learning-based classifier trained on 2,530 curated var genes, to accurately assign upstream groups using sequence features from different partial gene regions. We compared seven different methods, including support vector machines, random forest, XGB boost and HMMer models. The best model of upsAI for DBLα-tags sequences achieves an overall accuracy of 83%, 92% and for full-length var genes, therefore significantly outperforming existing tools. Further, we propose a new model to distinguish between internal and subtelomeric var genes with high accuracy and scalability. upsAI is available at https://github.com/sii-scRNA-Seq/upsAI , providing a robust and efficient resource for large-scale var gene analysis. It can classify var genes from 20 genomes in under one second.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 it