Scholarlink

<p>The extensive heterogeneity of cancer across biological scales necessitates a holistic approach beyond single-analyte methods. Integrating multi-omics data — from genomics to proteomics — with multimodal information, such as clinical records and medical imaging, offers a comprehensive, systems-level view of tumorigenesis. Artificial intelligence (AI) has emerged as the essential technology to decipher these complex, high-dimensional datasets, powering substantial advances in early diagnosis, precise patient stratification, prediction of therapeutic response and the elucidation of mechanisms of drug resistance. To translate these powerful predictive models into practice, explainable AI is critical for building clinical trust and generating novel, testable biological hypotheses. While challenges in data accessibility and model generalizability persist, the field is advancing toward patient-specific digital twins, promising to simulate individual disease trajectories and optimize treatments, thereby heralding a new era of precision oncology.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Advancing AI for multi-omics and clinical data integration in basic and translational cancer research

  • 期刊论文

  • 发表日期：

    2026年4月21日

查看全文
  • Fei Liu,
  • Stephan Beck,
  • Lei Yang,
  • Huiyan Luo,
  • Kang Zhang

摘要

The extensive heterogeneity of cancer across biological scales necessitates a holistic approach beyond single-analyte methods. Integrating multi-omics data — from genomics to proteomics — with multimodal information, such as clinical records and medical imaging, offers a comprehensive, systems-level view of tumorigenesis. Artificial intelligence (AI) has emerged as the essential technology to decipher these complex, high-dimensional datasets, powering substantial advances in early diagnosis, precise patient stratification, prediction of therapeutic response and the elucidation of mechanisms of drug resistance. To translate these powerful predictive models into practice, explainable AI is critical for building clinical trust and generating novel, testable biological hypotheses. While challenges in data accessibility and model generalizability persist, the field is advancing toward patient-specific digital twins, promising to simulate individual disease trajectories and optimize treatments, thereby heralding a new era of precision oncology.