Relationship inference from sparse data is an important task with applications ranging from product recommendation to drug discovery. A recently proposed linear model for sparse matrix completion has demonstrated surprising advantage in speed and accuracy over more sophisticated recommender systems algorithms. Here we extend the linear model to develop a shallow autoencoder for the dual neighborhood-regularized matrix completion problem. We demonstrate the speed and accuracy advantage of our approach over the existing state-of-the-art in predicting drug-target interactions and drug-disease associations.

翻译：从稀疏数据中推断关系是一项重要任务，其应用范围涵盖产品推荐到药物发现。最近提出的一种用于稀疏矩阵补全的线性模型，在速度和准确性上相比更复杂的推荐系统算法展现出显著优势。本文将该线性模型扩展，构建了一种浅层自编码器，用于解决双邻域正则化矩阵补全问题。我们证明了该方法在预测药物-靶标相互作用和药物-疾病关联方面，相比现有最先进技术具有速度和准确性上的优势。