Integration of k-means clustering algorithm with network analysis for drug-target interactions network prediction
Bibliographic record
Abstract
Prediction of the interactions between drugs and target proteins is an important factor in silico drug discovery. The number of known interactions is very small in comparison to the potential number of interactions. In this paper, a new method is proposed which combines data from both chemical structures and genomic sequence data. This method uses both supervised and unsupervised learning, as well as network analysis techniques. The proposed approach integrates k-means clustering algorithm with Social Network Analysis (SNA) techniques for a novel prediction of drug-target interactions. Here, we demonstrate the performance of our approach in the prediction of drug-target interactions by using four classes of drug-target interaction networks in human; enzymes, ion channels, G protein-coupled receptors (GPCRs), and nuclear receptors. The AUC curve is used to evaluate the accuracy of the proposed approach using three classifiers; Bayes Network, Naïve Bayes and SVM. We could identify novel drug-protein interactions using the Bayes network classifier. The reported accuracy for enzymes, ion channels, GPCRs, and nuclear receptors are 98%, 85%, 98.6% and 99.2%.
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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 | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".