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 of drug stimulation and gene knockout from LINCS project and developed a framework with a deep-learning-based model to predict potential interactions. The evaluation results showed that not only did our framework fit data with better accuracy than other classical methods, but predicted more credible drug-target interactions. What's more, the prediction has high percentage of overlap interactions across other platforms.