Drug-Target Interactions Prediction Using Network Based Method and Machine Learning
ISEF · 2022 Computational Biology and Bioinformatics
Overview
The lack of effective treatments for many diseases increases the demand for new drug discovery. Consequently, the drug repositioning (DR) strategy is widely used. Drug-Target Interaction (DTI) prediction is a crucial step in DR since it excludes a significant number of experiments on false interactions. Due to their reliability and high success rate, computational methods for prediction are used. In this project, a network-based method is developed to predict novel DTIs using machine learning and a graph embedding technique, namely Node2vec, with high prediction performance. The method constructs a heterogeneous network by integrating three graphs (drug-drug similarity, target-target similarity, and DTIs) that are given within the golden standard Yamanishi datasets. Then Node2Vec is applied to auto-generate feature representation for each drug and target depending on its dataset using the optimum parameters. Subsequently, to obtain the features for each drug-target pair a fusion function was implemented. Finally, the model’s performance in drug-target link prediction was evaluated by utilizing multiple classifiers. Compared to previous network-based methods, this method outperformed most of them by achieving the highest accuracy for each dataset, Nuclear receptor: 98%, G Protein-coupled receptor: 99%, Ion channel: 99%, and Enzyme: 99%. The application of the project is to find an efficient treatment in a shorter period because by predicting new DTIs, the search space toward the candidate drugs will be narrowed down.
Competition history
- ISEF 2022
Resources
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Source: Regeneron International Science and Engineering Fair