Systematic comparison of multi-omics survival models reveals a widespread lack of noise resistance

David Wissel; Daniel Rowson; Valentina Boeva

doi:10.1016/j.crmeth.2023.100461

Systematic comparison of multi-omics survival models reveals a widespread lack of noise resistance

Cell Rep Methods. 2023 Apr 24;3(4):100461. doi: 10.1016/j.crmeth.2023.100461.

Authors

David Wissel^{1

2

3}, Daniel Rowson¹, Valentina Boeva^{1

3

4}

Affiliations

¹ ETH Zurich, Department of Computer Science, Zurich, Switzerland.
² University of Zurich, Department of Molecular Life Sciences, Zurich, Switzerland.
³ SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
⁴ Université de Paris UMR-S1016, Institut Cochin, Inserm U1016, Paris, France.

Abstract

As observed in several previous studies, integrating more molecular modalities in multi-omics cancer survival models may not always improve model accuracy. In this study, we compared eight deep learning and four statistical integration techniques for survival prediction on 17 multi-omics datasets, examining model performance in terms of overall accuracy and noise resistance. We found that one deep learning method, mean late fusion, and two statistical methods, PriorityLasso and BlockForest, performed best in terms of both noise resistance and overall discriminative and calibration performance. Nevertheless, all methods struggled to adequately handle noise when too many modalities were added. In summary, we confirmed that current multi-omics survival methods are not sufficiently noise resistant. We recommend relying on only modalities for which there is known predictive value for a particular cancer type until models that have stronger noise-resistance properties are developed.

Keywords: cancer; deep learning models; multi-modal integration; multi-omics; noise resistance; statistical models; survival analysis.

MeSH terms

Calibration
Multiomics*