Abstract
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/.
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Acknowledgments
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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Deng, J., Yuan, Q., Mamitsuka, H., Zhu, S. (2018). DrugE-Rank: Predicting Drug-Target Interactions by Learning to Rank. In: Mamitsuka, H. (eds) Data Mining for Systems Biology. Methods in Molecular Biology, vol 1807. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8561-6_14
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DOI: https://doi.org/10.1007/978-1-4939-8561-6_14
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