Deep belief network–Based Matrix Factorization Model for MicroRNA-Disease Associations Prediction
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small single-stranded noncoding RNAs that have shown to play a critical role in regulating gene expression. In past decades, cumulative experimental studies have verified that miRNAs are implicated in many complex human diseases and might be potential biomarkers for various types of diseases. With the increase of miRNA-related data and the development of analysis methodologies, some computational methods have been developed for predicting miRNA-disease associations, which are more economical and time-saving than traditional biological experimental approaches. In this study, a novel computational model, deep belief network (DBN)-based matrix factorization (DBN-MF), is proposed for miRNA-disease association prediction. First, the raw interaction features of miRNAs and diseases were obtained from the miRNA-disease adjacent matrix. Second, 2 DBNs were used for unsupervised learning of the features of miRNAs and diseases, respectively, based on the raw interaction features. Finally, a classifier consisting of 2 DBNs and a cosine score function was trained with the initial weights of DBN from the last step. During the training, the miRNA-disease adjacent matrix was factorized into 2 feature matrices for the representation of miRNAs and diseases, and the final prediction label was obtained according to the feature matrices. The experimental results show that the proposed model outperforms the state-of-the-art approaches in miRNA-disease association prediction based on the 10-fold cross-validation. Besides, the effectiveness of our model was further demonstrated by case studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
| 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".