Development of disease diagnosis technology based on coattention cross-fusion of multiomics data.

BACKGROUND: Early diagnosis is vital for increasing the rates of curing diseases and patient survival in medicine. With the advancement of biotechnology, the types of bioomics data are increasing. The integration of multiomics data can provide more comprehensive biological information, thereby achieving more accurate diagnoses than single-omics data can. Nevertheless, current multiomics research is often limited to the intelligent diagnosis of a single disease or a few types of omics data and lacks a multiomics disease diagnosis model that can be widely applied to different diseases. Therefore, developing a model that can effectively utilize multiomics data and accurately diagnose diseases has become an important challenge in medical research.

RESULTS: On the basis of vibrational spectroscopy and metabolomics data, this study proposes an innovative coattention cross-fusion model for disease diagnosis on the basis of interactions of multiomics data. The model not only integrates the information of different omics data but also simulates the interactions between these data to achieve accurate diagnosis of diseases. Through comprehensive experiments, our method achieved accuracies of 95.00 %, 94.95 %, and 97.22 % and area under the curve (AUC) values of 95.00 %, 96.77 %, and 99.31 % on the cervical lymph node metastasis of the thyroid, systemic lupus erythematosus, and cancer datasets, respectively, indicating excellent performance in the diagnosis of multiple diseases.

SIGNIFICANCE: The proposed model outperforms existing multiomics models, enhancing medical diagnostic accuracy and offering new approaches for multiomics data use in disease diagnosis. The innovative coattention cross-fusion module enables more effective multiomics data processing and analysis, serving as a potent tool for early and precise disease diagnosis with substantial clinical and research implications.