Article

Evaluating Multiomics Integration Architectures for Training With Structured Missingness

2025-09-12

Abstract excerpt

Multimodal bioinformatics datasets are increasingly common in biomedical research, for tasks such as cancer subtyping and outcome prediction. It is feasible that data from a given patient, or even all data from a given institution, does not have coverage across all modalities; the available data is contingent on both the assay choice at the institution alongside technical aspects and associated drop-out. Consequen...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: f15b6542-0a7e-55d0-afc1-63e6df651cbd
DOI: 10.1101/2025.09.10.672554

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Evaluating Multiomics Integration Architectures for Training With Structured MissingnessDOI 10.1101/2025.09.10.672554Read

Quantifying the advantage of multimodal data fusion for survival prediction in cancer patientsSemantic proximity Read

Multimodal Machine Learning in Healthcare: A Tutorial and ReviewSemantic proximity Read

SurvBoard: Standardised Benchmarking for Multi-omics Cancer Survival ModelsSemantic proximity Read

miss-SNF: a multimodal patient similarity network integration approach to handle completely missing data sourcesSemantic proximity Read

MIDAS: a deep generative model for mosaic integration and knowledge transfer of single-cell multimodal dataSemantic proximity Read

Flexynesis: A deep learning framework for bulk multi-omics data integration for precision oncology and beyondSemantic proximity Read

MESSI: Multimodal Experiments with SyStematic Interrogation using nextflowSemantic proximity Read

Multimodal single cell data integration challenge: results and lessons learnedSemantic proximity Read

A Pairwise Imputation Strategy for Retaining Predictive Features When Combining Multiple DatasetsSemantic proximity Read

MultiSurv: Long-term cancer survival prediction using multimodal deep learningSemantic proximity Read

Deep Learning Architectures for Multi-Omics Data Integration: Bridging Biomarker Discovery and Clinical TranslationSemantic proximity Read

Lung cancer subtype diagnosis using weakly-paired multi-omics dataSemantic proximity Read