SMODA: Multimodal Omics Data for Esophageal Cancer Classification

SMODA is a framework integrating multimodal omics data via heterogeneous transfer learning. The associated dataset likely contains molecular data used for disease classification and subtype discovery, as demonstrated on an esophageal cancer dataset. The framework was authored by Jinhui Zhao and last updated on April 10, 2026.

Use Cases

• Benchmarking multimodal omics integration methods based on the described framework.
• Training disease classification models based on shared latent representations across modalities.
• Identifying novel disease subtypes based on altered lipid metabolism and inflammatory response features.
• Analyzing links between molecular subtypes and clinical outcomes based on the described application.

Strengths

• Framework is publicly available at a specific GitHub repository.
• Systematic benchmarking demonstrates performance in disease classification and subtype identification.
• Application shows better classification performance on a specific esophageal cancer dataset.

Limitations

• Dataset size is 24.4 KB, indicating a very limited scope.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

figshare

Freshness

Last updated 2026-04-10 08:29:30; freshness should be verified.