**Table 6.**

*Computational approaches for biomarkers in T1D.*

the multifactorial nature of T1D, no single biomarker can provide adequate power to predict the disease. Therefore, research into T1D prevention has to rely on the combination of multiple markers. Advances in high throughput omic technologies such as GWAS and NAPPA arrays have offered new opportunities to discover such biomarkers. In addition, advanced computational techniques including machine learning are being increasingly utilized to analyze numerous biomarkers in large data sets. Despite these advances, there is still an urgent need for new and improved biomarkers for T1D prediction and prevention. Surrogate biomarkers are needed to access the outcomes of preventative therapy trials in their early stages. Due to the long asymptomatic period for diabetes, it is too expensive and time-consuming for clinical trials to wait for the final clinical outcome. The lack of suitable surrogate biomarkers for T1D has severely hampered progress in clinical trials. Newer markers will also need to provide information on response to treatment in existing T1D patients. This information will aid in predicting which patients would benefit from specific therapies.

The simultaneous consideration of genomic, transcriptomic, and proteomic data, using advanced computational techniques will be required for accurate assessment of T1D risk and monitoring of therapeutic outcomes in the future.
