||The aspect of limited temporal data is one of the many challenges when dealing with clinical data. The amount of data that can be practically collected in everyday patients during the therapy is very limited due to the financial cost and the patient’s burden. This motivates us to transfer the mathematical and computational models to meet the challenges in clinical data, before we use them to guide patient therapy via prediction. In this talk, I will discuss two modeling approaches to tackle this problem. In the first part, I will discuss a Bayesian information-theoretic approach to determine effective scanning protocols of cancer patients. We propose a modified mutual information function with a temporal penalty term to account for the loss of temporal data. The effectiveness of our framework is demonstrated in determining image scanning scheduling for radiotherapy patients. In the second part, I will discuss modeling work using high-dimensional single-cell gene sequencing data. While this high-throughput genetic data brings in new opportunities in mathematical modeling of biological systems, due to the high cost of obtaining gene sequencing data, temporal data also lacks. We develop a cell state dynamics model from single-cell RNA sequencing data and show that our model can be used to describe the temporal dynamics of the cell landscape and study genetic perturbation with low cost. We show an example of modeling a hematopoiesis system and simulating abnormal differentiation that corresponds to acute myeloid leukemia.