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

arXiv: https://arxiv.org/abs/2302.03991 · HTML: https://mlsanity.com/paper/2302.03991/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 42 references that are in the map.
- First posted on arXiv 8 Feb 2023.

## Builds on (most cited first, 8 of 8)

- [Structured Matrix Completion with Applications to Genomic Data Integration](https://mlsanity.com/paper/1504.01823/) (2015)
- [Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results](https://mlsanity.com/paper/2106.02447/) (2021)
- [Multiple Imputation via Generative Adversarial Network for High-dimensional Blockwise Missing Value Problems](https://mlsanity.com/paper/2112.11507/) (2021)
- [Large-scale benchmark study of survival prediction methods using multi-omics data](https://mlsanity.com/paper/2003.03621/) (2020)
- [Integrating multi-source block-wise missing data in model selection](https://mlsanity.com/paper/1901.03797/) (2019)
- [Generalized Integrative Principal Component Analysis for Multi-Type Data with Block-Wise Missing Structure](https://mlsanity.com/paper/1808.02193/) (2018)
- [Supervised Learning for Multi-Block Incomplete Data](https://mlsanity.com/paper/1901.04380/) (2019)
- [Imputations for High Missing Rate Data in Covariates via Semi-supervised Learning Approach](https://mlsanity.com/paper/2205.07302/) (2022)

## Built on by (most cited first, 2 of 2)

- [A Complete Characterisation of Structured Missingness](https://mlsanity.com/paper/2307.02650/) (2023)
- [Integrate Any Omics: Towards genome-wide data integration for patient stratification](https://mlsanity.com/paper/2401.07937/) (2024)

## Abstract (arXiv)

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