Dual-Channel Learning Framework for miRNA-Drug Interaction Prediction Based on Structural Features and Signed Bipartite Graph Neural Network
Why this work is in the frame
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Bibliographic record
Abstract
MicroRNAs (miRNAs) play a vital role in regulating a wide range of biological functions and are key players in the development of many complex human diseases, making them novel therapeutic targets for drug development. Given the high expenses and time demands of traditional experimental methods, it is essential to develop efficient computational approaches for predicting miRNA-drug interactions (MDIs). This article presents a dual-channel learning framework, SSMDI, based on structural features and Signed Bipartite Graph Neural Network (SBGNN) for predicting MDIs. Firstly, Graph Isomorphism Networks (GIN) is employed to extract molecular graph features of drugs. Meanwhile, a combined framework of Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM) network and Self-attention Mechanism is utilized to capture sequence features of miRNAs. Compared with traditional networks, signed networks can deliver richer semantic information in drugs and miRNAs. Therefore, SBGNN is then used to aggregate and update the signed topological features of miRNAs and drugs. Finally, structural and signed topological features are integrated to predict MDIs. The predictive performance of the model is evaluated using 5-fold cross-validation (CV), achieving AUC of 0.9447 and AUPR of 0.9238. The case study further demonstrates the effectiveness of SSMDI in predicting MDIs. In summary, the SSMDI model proves to be an accurate tool for predicting MDIs, which holds significant implications for drug development and miRNA-based therapeutic research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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