We introduce a novel Bayesian hybrid matrix factorisation model (HMF) for data integration, based on combining multiple matrix factorisation methods, that can be used for in- and out-of-matrix prediction of missing values. The model is very general and can be used to integrate many datasets across different entity types, including repeated experiments, similarity matrices, and very sparse datasets. We apply our method on two biological applications, and extensively compare it to state-of-the-art machine learning and matrix factorisation models. For in-matrix predictions on drug sensitivity datasets we obtain consistently better performances than existing methods. This is especially the case when we increase the sparsity of the datasets. Furthermore, we perform out-of-matrix predictions on methylation and gene expression datasets, and obtain the best results on two of the three datasets, especially when the predictivity of datasets is high.

引入了一种新的贝叶斯混合矩阵分解模型（HMF）用于数据集成，能够结合多种矩阵分解方法，可用于预测缺失值的内部和外部矩阵，同时可集成许多不同实体类型的数据集，包括重复实验、相似度矩阵和非常稀疏的数据集。我们将方法应用于两个生物应用，并与最先进的机器学习和矩阵分解模型进行广泛比较。对于药物敏感性数据集的内部矩阵预测，我们获得了比现有方法更优异的性能，特别是当我们增加数据集的稀疏性时。此外，我们还对甲基化和基因表达数据集进行了外部矩阵预测，在其中三个数据集中有两个获得了最佳结果，特别是当数据集的可预测性高时。

数据整合的贝叶斯混合矩阵分解