{
  "id": 3021166,
  "title": "IMMF: An Interpretable Multi-Modal Framework for Hypothesis-Driven Biomarker Discovery in Triple-Negative Breast Cancer Using Public Data",
  "url": "https://urgent.news/2026/08/24/immf-an-interpretable-multi-modal-framework-for-hypothesis-driven",
  "topic": "health",
  "section": "Health & Medicine",
  "published": "2026-08-24T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.08.19.745809v1?rss=1"
  },
  "original_language": "en",
  "account": "Triple-Negative Breast Cancer (TNBC) is known for its high heterogeneity, poor prognosis, and limited targeted treatment options. The connection between molecular alterations and histopathological morphology poses a significant challenge in precision oncology. To address this issue, researchers have developed an interpretable, multi-modal framework that combines histopathological image analysis with multi-omics profiling, including somatic mutations, DNA methylation, and copy number alterations. This innovative approach utilizes U-Net-based nuclei segmentation, vision-language models such as BLIP, biomedical language models like BioGPT, and explainable AI techniques like SHAP and LIME.\n\nThe framework demonstrates impressive predictive performance with an AUC of 0.989, while also offering transparent and biologically grounded interpretations. By integrating morphological features with genomically prioritized biomarkers, the framework confirms established TNBC drivers and generates novel, testable hypotheses that link specific epigenetic alterations to distinct morphological phenotypes. Although causal validation through wet-lab experiments is required for further confirmation, the proposed framework significantly accelerates hypothesis-driven biomarker discovery. By integrating diverse data modalities with language-based reasoning, the framework provides a clear and transparent foundation for hypothesis generation and potential clinical translation in the field of breast cancer research.",
  "summary": "Triple-Negative Breast Cancer (TNBC) is characterized by high heterogeneity, poor prognosis, and limited targeted treatment options. Bridging the gap between molecular alterations and histopathological morphology remains a major challenge in precision oncology. We propose an interpretable, multi-modal framework that integrates histopathological image analysis with multi-omics profiling (somatic…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}