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Evidence record 10693 · automatically gathered

OMNIS: a spatially informed multi-omics deep-learning framework for tumor recurrence prediction and primary–metastatic tumor differentiation title page

BackgroundCancer recurrence and distant metastasis are major causes of cancer-related death, yet existing biomarkers and single-omics models have limited accuracy and interpretability across tumor types.MethodsWe developed OMNIS (OMics Network Integration and Spatial representation), a convolutional deep-learning framework that embeds multi-omics profiles into a five-channel genomic image ordered by Hi-C–derived chromosomal proximity. Somatic mutation, copy-number alteration, DNA methylation and

Record details

Published: 15 July 2026
Source: Frontiers in Artificial Intelligence
Category: Research
Topics: Safety & alignment · Biotech
Retrieved: 16 July 2026

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These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

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How to cite this record

ethics.ai (15 July 2026), “OMNIS: a spatially informed multi-omics deep-learning framework for tumor recurrence prediction and primary–metastatic tumor differentiation title page,” evidence record 10693, https://ethics.ai/record/10693 (originally published by Frontiers in Artificial Intelligence).

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