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Background The ability to predict the interaction of drugs with target proteins is essential to research and development of drug. However, the traditional experimental paradigm is costly, and previous in silico prediction paradigms have been impeded by the wide range of data platforms and data scarcity. Results In this paper, we modeled the prediction of drug-target interactions as a binary classification...
Drug-target interaction identification is of highly importance in drug research and development. The traditional experimental paradigm is costly, while the previous in silico prediction paradigm remains a challenge because of diversified data production platforms and data scarcity. In this paper, we modeled drug-target interaction prediction as a binary classification task based on transcriptome data...
This paper describes our work for ALIDATA DISCOVERY competition. Through analyzing massive real-world user action data provided by Tmall, one of the largest B2C online retail platforms in China, we try to predict future user purchases. The prediction results are judged by F1 Score that is consist of two parts, precision and recall rate. The provided data set contains more than 500 million action records...
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