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As the availability of omics data has increased in the last few years, moremulti-omics data have been generated, that is, high-dimensional molecular dataconsisting of several types such as genomic, transcriptomic, or proteomic data,all obtained from the same patients. Such data lend themselves to being used ascovariates in automatic outcome prediction because each omics type maycontribute unique information, possibly improving predictions compared to usingonly one omics data type. Frequently, however, in the training data and thedata to which automatic prediction rules should be applied, the test data, thedifferent omics data types are not available for all patients. We refer to thistype of data as block-wise missing multi-omics data. First, we provide aliterature review on existing prediction methods applicable to such data.Subsequently, using a collection of 13 publicly available multi-omics datasets, we compare the predictive performances of several of these approaches fordifferent block-wise missingness patterns. Finally, we discuss the results ofthis empirical comparison study and draw some tentative conclusions.

Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study