药物再定位 (DR) 是一种策略，用于寻找现有药物的新靶点，它在减少传统药物开发成本、时间和风险方面起着重要作用。最近，在DR预测领域中广泛使用了矩阵分解方法。然而，仍存在两个挑战：1) 学习能力不足，该模型无法准确预测更多的潜在关联。2) 容易陷入糟糕的局部最优解，该模型倾向于获得次优结果。在本研究中，我们提出了一种自适应非负矩阵三因子分解 (SPLNMTF) 模型，通过非负矩阵三因子分解将来自患者、基因和药物的三种不同生物数据集成成异构网络，从而学习更多信息以提高模型的学习能力。同时，SPLNMTF模型以软加权的方式依次将样本从容易 (高质量) 到复杂 (低质量) 加入训练，有效减轻陷入糟糕的局部最优解的情况，从而提高模型的预测性能。在卵巢癌和急性髓系白血病 (AML) 的两个真实数据集上的实验结果表明，SPLNMTF模型优于其他八种最先进的模型，在药物重新定位方面获得更好的预测性能。数据和源代码可在以下网址获得：https://github.com/qi0906/SPLNMTF。

Drug repositioning (DR) is a strategy to find new targets for existing drugs, which plays an important role in reducing the costs, time, and risk of traditional drug development. Recently, the matrix factorization approach has been widely used in the field of DR prediction. Nevertheless, there are still two challenges: 1) Learning ability deficiencies, the model cannot accurately predict more potential associations. 2) Easy to fall into a bad local optimal solution, the model tends to get a suboptimal result. In this study, we propose a self-paced non-negative matrix tri-factorization (SPLNMTF) model, which integrates three types of different biological data from patients, genes, and drugs into a heterogeneous network through non-negative matrix tri-factorization, thereby learning more information to improve the learning ability of the model. In the meantime, the SPLNMTF model sequentially includes samples into training from easy (high-quality) to complex (low-quality) in the soft weighting way, which effectively alleviates falling into a bad local optimal solution to improve the prediction performance of the model. The experimental results on two real datasets of ovarian cancer and acute myeloid leukemia (AML) show that SPLNMTF outperforms the other eight state-of-the-art models and gets better prediction performance in drug repositioning. The data and source code are available at: https://github.com/qi0906/SPLNMTF.