Computational Drug Repositioning Based on Integrated Similarity Measures and Deep Learning
Bibliographic record
Abstract
Drug repositioning is an emerging approach in pharmaceutical research for identifying novel therapeutic potentials for approved drugs and discover therapies for untreated diseases. Due to its time and cost efficiency, drug repositioning plays an instrumental role in optimizing the drug development process compared to the traditional \textit{de novo} drug discovery process. Advances in the genomics, together with the enormous growth of large-scale publicly available data and the availability of high-performance computing capabilities, have further motivated the development of computational drug repositioning approaches. Numerous attempts have been carried out, with different degrees of efficiency and success, to computationally study the potential of identifying alternative drug indications, which slow, stop, or reverse the courses of incurable diseases. More recently, the rise of machine learning techniques, together with the availability of powerful computers, has made the area of computational drug repositioning an area of intense activities. In this thesis, the integration of various biological and biomedical data from different sources to improve the quality of biomedical knowledge in the computational drug repositioning field is addressed. The main contribution of this thesis is four-fold. First, it provides a comprehensive review of drug repositioning strategies, resources, and computational approaches. Second, it develops an approach for identifying disease-specific gene associations, which can be further used as a resource for computational drug repositioning methods. Third, it proposes a robust framework that utilizes known drug-disease interactions and drug-related similarity information to predict new drug-disease interactions. Fourth, it introduces a novel integrative framework for predicting drug-disease interactions using known drug-disease interactions, drug-related similarity information, and disease-related similarity information. The two proposed frameworks leverage advanced similarity calculation, selection, and integration to understand the functional and behavioural correlation between drugs and diseases. Furthermore, they employ the most advanced machine learning tools in predicting hidden or indirect drug-disease interactions for potential drug repositioning applications.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".