  Recent years have seen a surge of novel neural network architectures for the
integration of multi-omics data for prediction. Most of the architectures
include either encoders alone or encoders and decoders, i.e., autoencoders of
various sorts, to transform multi-omics data into latent representations. One
important parameter is the depth of integration: the point at which the latent
representations are computed or merged, which can be either early,
intermediate, or late. The literature on integration methods is growing
steadily, however, close to nothing is known about the relative performance of
these methods under fair experimental conditions and under consideration of
different use cases. We developed a comparison framework that trains and
optimizes multi-omics integration methods under equal conditions. We
incorporated early integration and four recently published deep learning
methods: MOLI, Super.FELT, OmiEmbed, and MOMA. Further, we devised a novel
method, Omics Stacking, that combines the advantages of intermediate and late
integration. Experiments were conducted on a public drug response data set with
multiple omics data (somatic point mutations, somatic copy number profiles and
gene expression profiles) that was obtained from cell lines, patient-derived
xenografts, and patient samples. Our experiments confirmed that early
integration has the lowest predictive performance. Overall, architectures that
integrate triplet loss achieved the best results. Statistical differences can,
overall, rarely be observed, however, in terms of the average ranks of methods,
Super.FELT is consistently performing best in a cross-validation setting and
Omics Stacking best in an external test set setting. The source code of all
experiments is available under
\url{https://github.com/kramerlab/Multi-Omics_analysis}
