Breaking Barriers: Democratizing Machine Learning for RNA-Protein Interaction Prediction in Life Sciences
Bibliographic record
Abstract
À medida que o armazenamento de sequências biológicas aumenta, extrair informações torna-se crucial para avanços na saúde. A complexidade dessas sequências exige técnicas sofisticadas, como Aprendizado de Máquina (AM). No entanto, desenvolver soluções fortes de AM demanda conhecimento especializado, muitas vezes fora do alcance de muitos pesquisadores das ciências da vida, ampliando ainda mais as disparidades. Considerando isso, apresentamos o BioPrediction, um framework de AM ponta a ponta que cria modelos para identificar interações entre sequências, como pares de RNA não codificante e proteínas, sem intervenção humana. Os resultados destacam seu desempenho superior sobre modelos criados por especialistas em múltiplos conjuntos de dados. Essa automação abre novos caminhos para desvendar interações complexas e explorar mecanismos de doenças.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".