A review of computational model-based prediction of lncRNA subcellular localization
Bibliographic record
Abstract
Long non-coding RNAs (lncRNA) play pivotal roles in diverse cellular processes, and the determination of lncRNA subcellular localization serves as crucial information for elucidating their functional roles. However, conventional biochemical experimental methodologies employed for identifying lncRNA subcellular localization exhibit inherent complexities, challenges in reproducibility, and substantial costs. In the contemporary era of burgeoning bioinformatics, computational models for predicting the subcellular localization of biomolecules offer a viable alternative. Notably, these computational approaches boast high efficiency and relatively lower costs, presenting a substantial reduction in time and human resource expenditure compared to traditional experimental protocols. This comprehensive review encapsulates the latest strides in leveraging computational models for the prediction of lncRNA subcellular localization, offering novel avenues for a profound comprehension of lncRNA functionality and their intricate involvement in cellular processes.
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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) | 0.001 | 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 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".