Abstract
Introduction: Design and development of new drugs needs a huge amount of investment of time and money. The advent of machine learning and computational biology has led to sophisticated techniques for drug repositioning, i.e., recommending available drugs for new diseases or, more specifically, protein targets. However, there remains a critical need for improved synergy between these techniques to enhance their predictive accuracy and practical application in clinical settings.
Methods: This study presents a novel approach that integrates two methodologies: SLSDR, a sparse and low-redundant subspace learning-based dual-graph regularized robust feature selection technique, and the iDrug method for drug repurposing which integrates different domains. SLSDR is a subspace learning algorithm based on matrix factorization, and iDrug is a matrix factorization-based drug repositioning method that integrates data from two different domains (drug-disease and drug-target domains). By leveraging SLSDR's ability to extract essential features from drug-disease and drug-target spaces, we enhance the iDrug objective function. Our approach includes constructing a drug-drug similarity matrix using a feature space derived from SLSDR, and target-target and disease-disease similarity matrices. This ensures a comprehensive representation of drug-disease and drug-target associations. We introduce a novel objective function that captures the nuanced interactions between drugs and diseases, considering the complex interrelationships among features within all the datasets.
Results: By integrating these components, our strategy offers a holistic solution for drug repositioning, optimizing the prediction process. In terms of prediction accuracy, AUC, AUPR and computing efficiency, the results indicate notable gains over the state of the art drug repurposing methods. Figure 1, represents the comparison of the performance of the proposed method with existing approaches across various metrics.
Conclusion: The proposed matrix factorization based method for drug repurposing, benefits from integrating knowledge from two domains, drug-disease and drug-target domains, and also is capable of preserve the geometry of the data in both feature space, and s ample space. Comparing to existing state of the art methods, this shows accuracy improvement in drug repurposing.