DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification.

Background: Achieving precise cancer subtype classification is imperative for effective prognosis and treatment. Multi-omics studies, encompassing diverse data modalities, have emerged as powerful tools for unraveling the complexities of cancer. However, owing to the intricacies of biological data, multi-omics datasets generally show variations in data types, scales, and distributions.

Graph Neural Networks With Multiple Prior Knowledge for Multi-Omics Data Analysis.

With the development of biotechnology, a large amount of multi-omics data have been collected for precision medicine. There exists multiple graph-based prior biological knowledge about omics data, such as gene-gene interaction networks. Recently, there has been an increasing interest in introducing graph neural networks (GNNs) into multi-omics learning.

Health risks of Cryptosporidium and Giardia in the application of surface water and septic tank effluent in Chinese agriculture: Impact on cancer patients identified by quantitative microbial risk assessment.

The protozoa Cryptosporidium and Giardia are major causes of diarrhea and are commonly found on vegetables in China. They pose a health risk, particularly to immunocompromised individuals, including cancer patients. A quantitative microbial risk assessment of Chinese data evaluated the risks of Cryptosporidium and Giardia exposure arising from the application of surface water and septic tank effluent to agricultural land.

Spatially explicit model of the Cryptosporidium and Giardia disease burden from surface and ground waters in urban and rural areas of the Three Gorges Reservoir watershed in Chongqing, China.

Cryptosporidium and Giardia (major causes of diarrhea) are widely distributed in Chinese source waters and threaten human health. A new spatially explicit GloWPa-TGR-Crypt-Giar C1 model is presented to simultaneously estimate mean monthly (oo)cyst concentrations in surface and ground waters in the Three Gorges Reservoir (TGR) watershed. A quantitative risk assessment of protozoal infections considered different source waters, transmission pathways, regions, susceptible subpopulations, and drinking water treatments.