A review of machine learning-based prediction of lncRNA subcellular localization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
With the continuous development of the field of bioinformatics, the subcellular localization of long non-coding RNA (lncRNA) has become a highly prominent frontier. LncRNAs play crucial regulatory roles in cellular processes, and understanding their subcellular localization is essential for comprehending their functions and mechanisms. However, traditional experimental methods face challenges of high costs and time consumption when predicting the subcellular localization of lncRNAs on a large scale, which has led to the emergence of research methods based on machine learning. This review aims to recap the latest advancements and trends in machine learning-based prediction of lncRNA subcellular localization in recent years. It not only provides new opportunities for a better understanding of lncRNA functions and cellular processes but also propels advancements in the fields of bioinformatics and molecular biology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it