The Implementation of Deep Learning Algorithm with Gaussian Blur Data Preprocessing in Circular RNA Classification and Detection
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
Introduction: Circular RNAs (circRNAs) are increasingly recognized as key regulators of gene expression due to their unique closed-loop structure and involvement in various cellular processes. This study investigates the utilization of machine learning algorithms in predicting circRNA-disease associations. Methods: This study proposes a novel deep learning approach leveraging artificial neural networks (ANN) for circRNA classification. The methodology involves data collection from circRNA databases, k-mers counting for feature extraction, Gaussian blur implementation for data smoothing, and ANN-based model training. Results: Evaluation of the trained models based on precision, recall, and f1-score metrics shows an overall accuracy of 0.7511, with an average precision score of 0.7982, recall of 0.7511, and f1-score of 0.7637. Discussion: The results indicate that our ANN-based algorithm effectively detects and classifies circRNA datasets with considerable accuracy. Compared to the algorithm from past research, our algorithm is also shown to have less computational power. Conclusion: Comparative analysis demonstrates improved performance compared to previous algorithms, suggesting its potential for widespread implementation due to reduced computational requirements and simpler implementation.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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