Predicting Drug-Disease Association Based on Ensemble Strategy

Abstract

Drug repositioning is used to find new uses for existing drugs, effectively shortening the drug research and development cycle and reducing costs and risks. A new model of drug repositioning based on ensemble learning is proposed. This work develops a novel computational drug repositioning approach called CMAF to discover potential drug-disease associations. First, for new drugs and diseases or unknown drug-disease pairs, based on their known neighbor information, an association probability can be obtained by implementing the weighted K nearest known neighbors (WKNKN) method and improving the drug-disease association information. Then, a new drug similarity network and new disease similarity network can be constructed. Three prediction models are applied and ensembled to enable the final association of drug-disease pairs based on improved drug-disease association information and the constructed similarity network. The experimental results demonstrate that the developed approach outperforms recent state-of-the-art prediction models. Case studies further confirm the predictive ability of the proposed method. Our proposed method can effectively improve the prediction results.

Drug Repurposing Using Modularity Clustering in Drug-Drug Similarity Networks Based on Drug–Gene Interactions

“…Even considering one type of information has become significantly complex; for instance, the drug-drug interaction networks in DrugBank 3.0 had an average degree of ∼20, and in DrugBank 5.1.8 the average DDI network degree is ∼600). Recent literature [68][69][70] advances the so-called ensemble methods to address this new situation of being confronted with an overabundance rather than scarcity of data (see Section 4.4). Employing computational methods (i.e., data mining and machine learning) in drug repositioning is generally hampered because we do not a have robust ground truth.…”

“…The problem of drug repositioning is also very complex; however, prediction accuracy is not the primary indicator of success (the benefit of correctly predicting even a few drug repositionings is more significant than the cost of experiments entailed by testing the wrong predictions [74].) As such, very recent literature advances the idea of using ensemble methods for drug repositioning [69,70].…”

Cluster-based text mining for extracting drug candidates for the prevention of COVID-19 from the biomedical literature

“…They applied an ensemble strategy to predict the association of drug-disease pairs based on improved drug-disease association information and the constructed similarity network. 9 Instead of classifying and predicting the disease-drug associations, we grouped similar disease terms into clusters, and then using a frequency analysis approach, we registered candidate drugs to search for their presence in the COVID-19 DrugBank database.…”

Biomolecular Network-based Study of a Parasitic Disease and Therapeutic Drugs

“…In general, computer-aided models of drug design and repurposing, simulation, and computer vision technologies have come to play an important role in systems biology and medicine research to understand complicated molecular interaction mechanisms and complex regulatory functions. A variety of modelling tools have been developed to simulate biochemical interactions, gene transcription kinetics, metabolic control, and drug delivery pathway mechanisms, which helps us to systematically test, and experimentally verify knowledge of biological and medicinal processes, [8,31,40,43]. Network-based modelling technique is a suitable tool for drug and disease-related studies, [11,20,30,39,41].…”