  Multiomics data fusion integrates diverse data modalities, ranging from
transcriptomics to proteomics, to gain a comprehensive understanding of
biological systems and enhance predictions on outcomes of interest related to
disease phenotypes and treatment responses. Cooperative learning, a recently
proposed method, unifies the commonly-used fusion approaches, including early
and late fusion, and offers a systematic framework for leveraging the shared
underlying relationships across omics to strengthen signals. However, the
challenge of acquiring large-scale labeled data remains, and there are cases
where multiomics data are available but in the absence of annotated labels. To
harness the potential of unlabeled multiomcis data, we introduce
semi-supervised cooperative learning. By utilizing an "agreement penalty", our
method incorporates the additional unlabeled data in the learning process and
achieves consistently superior predictive performance on simulated data and a
real multiomics study of aging. It offers an effective solution to multiomics
data fusion in settings with both labeled and unlabeled data and maximizes the
utility of available data resources, with the potential of significantly
improving predictive models for diagnostics and therapeutics in an increasingly
multiomics world.
