DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank
Identifying drug-target interactions is crucial for the success of drug discovery. Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. By utilizing the “Learning to rank” framework, we propose a new method, DrugE-Rank, to combine these two different types of methods for improving the prediction performance of new candidate drugs and targets. DrugE-Rank is available at http://datamining-iip.fudan.edu.cn/service/DrugE-Rank/.
Key wordsDrugE-Rank Learning to rank Drug discovery
This work has been partially supported by National Natural Science Foundation of China (Grant Nos: 61572139), MEXT KAKENHI #16H02868, and FiDiPro by Tekes.
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