EMDS: predicting essential miRNAs based on deep learning and sequences
Bibliographic record
Abstract
MicroRNAs (miRNAs) as small 19- to 24-nucleotide noncoding RNAs play crucial roles in some key biological progress associated with human diseases. Therefore, identifying the essentiality of miRNAs is important to systematically understand the pathogenic mechanism of diseases. There are some computational methods have been developed to predict essential miRNAs because traditional biological experiments are both time- and labor-consuming. However, these computational methods only used the statistical feature and structural feature of miRNA sequences. The timing characteristics of sequences also should be considered to improve the prediction performance. In addition, the capability deep learning model is well-known. Therefore, in this study, we present a computational method (called EMDS) to predict essential miRNAs. EMDS takes not only the statistical and structural features of sequences but also the subsequence features based on the time characteristics of sequences and Convolutional Neural Networks (CNN). Furthermore, considering that the successful applications of attention mechanism and the subsequence in a miRNA sequence are important, we use a neural attention mechanism to obtain subsequence features of miRNAs. Finally, we integrate the statistical features and structural features, subsequence features as final miRNA features which is inputted into Light Gradient Boosting Machine (LGBM) to predict essential miRNAs. We evaluate the prediction performance of our method by the 5-fold cross validation. We also compare EMDS with other four competing methods which include PESM, miES, Gaussian Naive Bayes (Gaus_NB) and Support Vector Machine (SVM) by performing same cross validation experiments. The results show that EMDS achieves better prediction performance in terms of AUC (EMDS:0.9335, PESM:0.9117, miES:0.8837, Gaus_NB:0.8720, SVM:0.8571). It also illustrates that our method can effectively predict the essential miRNAs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".