An omics-to-omics joint knowledge association subtensor model for radiogenomics cross-modal modules from genomics and ultrasonic images of breast cancers

The radiogenomics analysis can provide the connections between genomics and radiomics, which can infer the genomic features of tumors from their radiogenomic associations through the low-cost and non-invasiveness screening ultrasonic images. Although there are a number of pioneer approaches exploring the connections between genomic aberrations and ultrasonic features, these studies mainly focus on the relationship between ultrasonic features and only the most popular cancer genes, confronting two difficulties: missing many-to -many relationships as omics-to-omics view, and confounding group-specific associations with whole sample associations. To overcome the difficulty of omics-to-omics view and the issue of tumor heterogeneity, we propose an omics-to-omics joint knowledge association subtensor model. Specifically, the subtensor factorization framework can successfully discover the joint cross-modal module via an omics-to-omics view, while the sparse weight sample indication strategy can mine sample subgroups from the multi-omic data with tumor heterogeneity. The experimental evaluation result shows the jointness of the discovered modules across omics, their association with tumorigenesis contribution, and their relation for cancer related functions. In summary, our proposed omics-to-omics joint knowledge association subtensor model can serve as an efficient tool for radiogenomic knowledge associations, promoting the cross-modal knowledge graph construction of in explainable artificial intelligence cancer diagnosis.

Automated summary

Radiogenomics analysis can infer the genomic features of tumors from their radiogenomic associations through low-cost and non-invasive screening ultrasonic images, providing connections between genomics and radiomics. Existing studies mainly focus on the relationship between ultrasonic features and popular cancer genes, but overlook the many-to-many relationships and sample associations with tumor heterogeneity. To address these challenges, we propose an omics-to-omics joint knowledge association subtensor model that discovers cross-modal modules and identifies sample subgroups. Experimental results demonstrate the jointness of discovered modules, their association with tumorigenesis contribution, and their relation to cancer-related functions. In conclusion, our proposed model can effectively facilitate radiogenomic knowledge associations and promote the construction of explainable AI cancer diagnosis.