AI Learns to Predict Patient Outcomes Even When Key Omics Data Are Missing

Precision medicine has long promised a future in which a patient’s treatment is tailored to the molecular signatures written into their genome, transcriptome, epigenome, proteome and metabolome. In theory, combining these layers of biological information—the field known as multi-omics integration—should let algorithms forecast how a disease will progress, which drugs will work and which patients are at highest risk. In practice, however, real-world clinical cohorts almost never deliver the complete, neatly paired datasets that many machine learning models quietly assume. A new review published in Artificial Intelligence Review by Ricky Nguyen and Fatemeh Vafaee of the University of New South Wales in Sydney examines the growing family of techniques designed to keep multi-omics prediction working when entire layers of data are simply missing.

The problem the researchers describe is known as block-wise modality missingness, and it is endemic to clinical research. Sequencing a genome, profiling the methylome or running a mass-spectrometry-based proteomic assay each carries its own costs, technical demands and failure rates. A hospital may afford whole-exome sequencing for every patient in a cancer cohort but collect RNA-sequencing data for only a subset. Assays fail. Study designs evolve mid-project, adding omics layers that earlier patients never received. The result is a data matrix riddled with entire missing blocks rather than scattered gaps—and this pattern is far more damaging to conventional integrative pipelines than ordinary single-cell missingness.

Most existing multi-omics methods were built with fully paired data in mind. When confronted with incomplete cohorts, practitioners typically fall back on one of two workarounds: complete-case filtering, in which every patient lacking any omics layer is discarded, or point-wise imputation, in which missing values are filled in one element at a time. Both strategies carry serious drawbacks. Complete-case analysis can shrink a cohort so drastically that statistical power collapses, and it systematically biases the remaining sample toward patients who received the most thorough work-up—often those with better access to care or more advanced disease at diagnosis. Point-wise imputation, meanwhile, treats block-wise absence as if it were random noise, which it emphatically is not, and can manufacture false confidence in downstream predictions while masking genuine biological signal.

The review’s central contribution is a methodological taxonomy that organises the emerging solutions into coherent families. One prominent family comprises missingness-aware fusion architectures: models that explicitly encode which modalities are present for each patient and adapt their internal computations accordingly. Rather than demanding a full complement of inputs, these networks learn fusion functions that can operate on whatever subset of omics layers happens to be available, weighting contributions in a way that accounts for both the information content and the absence of particular views. The absence of a modality becomes a structured condition the model reasons about, rather than a defect it must repair.

A second family relies on shared latent representations with subset-conditioned inference. Here, the idea is to project each available omics layer into a common latent space where modalities become comparable and combinable. Because the encoding is learned jointly across patients with different patterns of availability, the model can capture the correlations that link, say, methylation patterns to transcriptomic states, and exploit those correlations when one view is missing. At inference time, the model conditions on the observed subset for a given patient and produces outcome predictions from that partial evidence. The approach borrows conceptually from multi-view learning and from variational frameworks in which each modality is treated as a partial observation of a single underlying biological state.

The third major category in the taxonomy is modality-completion: frameworks that attempt to synthesise the missing layer itself before integration proceeds. Generative models, including adversarial and autoencoder-based designs, learn the cross-modal relationships in the complete subset of the cohort and then produce plausible surrogates for missing omics profiles. Crucially, the review stresses that the goal is not to conjure the true molecular measurements of a patient who was never assayed, but to supply the downstream predictor with an estimate that preserves the predictive information the missing layer would have contributed. Done well, completion can recover much of the discriminative power lost to missingness; done poorly, it can inject hallucinated structure that inflates apparent accuracy without reflecting real biology.

Nguyen and Vafaee pay particular attention to cross-pollination from an unexpected corner of computational biology: single-cell research. Single-cell multi-omics experiments frequently produce mosaic datasets in which each cell is profiled for only a subset of modalities—RNA in one cell, chromatin accessibility in another—and an entire literature has arisen on integrating such fragmentary data. The review asks when the architectural tricks developed for that setting, such as modality dropout during training, cross-modality translation and shared embedding spaces, transfer to cohort-level supervised prediction of clinical outcomes. The authors conclude that the underlying mechanisms are often architecturally transferable, but that the statistical regimes differ: single-cell datasets contain thousands to millions of sparse observations, whereas clinical cohorts are typically small, heterogeneous and confounded by treatment and demographics, demanding greater caution and stronger regularisation.

Throughout, the review contrasts the design philosophies, inference mechanisms and robustness properties of competing approaches, and it makes clear that no single strategy dominates. Missingness-aware fusion tends to be the most conservative, never inventing data but sometimes sacrificing performance when a highly informative modality is absent. Latent-space methods offer flexibility and elegant handling of heterogeneous subsets but can be sensitive to how well the shared space is learned from limited samples. Completion frameworks can be the most powerful when cross-modal correlations are strong, yet they carry the greatest risk of propagating fabricated signal into clinical decisions. The right choice, the authors argue, depends on the missingness pattern itself—how it arises, whether it is informative, and which modalities it affects.

The practical stakes are considerable. As multi-omics assays move from research laboratories into routine oncology, immunology and rare-disease care, the models that guide treatment will inevitably be deployed on patients whose molecular work-ups are incomplete. A clinical prediction system that silently fails, or silently biases its estimates, whenever a modality is missing is not safe for bedside use. By mapping the assumptions each method makes about missingness—whether it treats absence as random, informative or structural—the review offers clinicians and bioinformaticians a principled framework for matching model class to data reality, and for recognising when a published benchmark built on artificially deleted data says little about performance in a genuinely incomplete cohort.

The work, which was supported by Australia’s CSIRO Next-Generation Graduate Program and the National Health and Medical Research Council, arrives as the field confronts a widening gap between the tidy datasets of methodological papers and the messy matrices of real hospitals. By clarifying when conventional integration breaks down, how missingness-aware designs hold together, and which lessons from single-cell genomics carry over to patient-level prediction, Nguyen and Vafaee provide a roadmap for building predictive models that meet clinical data as it actually exists: partial, uneven and imperfect, but still rich enough, if handled with the right mathematics, to improve the odds for the patients behind the numbers.

The review appears as an open-access publication, meaning its taxonomy and comparative analyses are freely available to researchers in low-resource settings who often face the very data limitations the paper addresses. The article was received in March 2026 and accepted in September 2026, placing it among the first comprehensive treatments of block-wise missingness in patient-level multi-omics prediction, a topic that has previously been scattered across methodological papers in machine learning venues and bioinformatics journals without a unifying framework.

One useful lens for understanding the review’s scope comes from its positioning within predictive medicine and biostatistics. Classical statistical approaches to incomplete data, such as likelihood-based methods that ignore missingness under certain assumptions, were developed for low-dimensional settings where a handful of covariates might be unobserved. Multi-omics data break these assumptions in two directions at once: the dimensionality is enormous, with tens of thousands of features per modality, and the missingness operates at the level of whole data layers rather than individual entries. This means that techniques from the missing-data literature in statistics cannot simply be imported wholesale, and the machine learning architectures surveyed in the review represent a genuinely new methodological territory rather than an incremental extension of older tools.

The authors’ institutional context is also relevant to the review’s perspective. Nguyen and Vafaee are based at UNSW Sydney’s School of Biotechnology and Biomolecular Sciences, with Vafaee additionally affiliated with the UNSW AI Institute, a setting that bridges experimental molecular biology and artificial intelligence research. The work was funded through CSIRO’s Next-Generation Graduate Program and the National Health and Medical Research Council, reflecting Australian investment in translational computational health research.

Accompanying the article are supplementary data files in spreadsheet format, which likely catalogue the methods included in the taxonomy and their characteristics, offering readers a practical reference tool when selecting approaches for their own incomplete cohorts. The article’s keyword set, spanning multimodal machine learning, missing-modality learning, incomplete multi-omics data integration and clinical outcome prediction, signals its intended audience across both computer science and clinical informatics communities, and the authors declare no competing interests.

Subject of Research: Machine learning methods for clinical outcome prediction from incomplete multi-omics datasets with missing modalities

Article Title: Modelling missing modalities in multi-omics clinical outcome prediction

Article References: Nguyen, R., & Vafaee, F. (2026). Modelling missing modalities in multi-omics clinical outcome prediction. Artificial Intelligence Review. https://doi.org/10.1007/s10462-026-11702-7

Image Credits: AI Generated

DOI: 10.1007/s10462-026-11702-7

Keywords: multi-omics, missing modality learning, clinical outcome prediction, machine learning, precision medicine, data integration, imputation, latent representations, single-cell genomics, biomarker discovery, incomplete data, predictive medicine