The Implementation of Deep Learning Algorithm with Gaussian Blur Data Preprocessing in Circular RNA Classification and Detection
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".