Disulfiram (DSF) is a thiocarbamate derivative that has been used for the treatment of alcoholism. Researchers recently found out that DSF, a medicine authorized by the FDA to prevent alcohol consumption, has been investigated for its potential in cancer treatment. DSF, when taken by mouth, undergoes extensive metabolism in the liver, making it unsuitable for cancer treatment via oral administration. Furthermore, DSF has demonstrated the ability to enhance the cytotoxic impact of anticancer medications while preserving healthy cells. To solve the problem of instability and insufficient therapeutic efficacy, invasomal vesicles developed with better entrapment efficiency and size to serve as a carrier for the transdermal delivery of disulf

Cancer is in general not a result of an abnormality of a single gene but a consequence of changes in many genes, it is therefore of great importance to understand the roles of different oncogenic and tumor suppressor pathways in tumorigenesis. In recent years, there have been many computational models developed to study the genetic alterations of different pathways in the evolutionary process of cancer. However, most of the methods are knowledge-based enrichment analyses and inflexible to analyze user-defined pathways or gene sets. In this paper, we develop a nonparametric and data-driven approach to testing for the dynamic changes of pathways over the cancer progression. Our method is based on an expansion and refinement of the pathway bei

Spatial data observed on a group of areal units is common in scientific applications. The usual hierarchical approach for modeling this kind of dataset is to introduce a spatial random effect with an autoregressive prior. However, the usual Markov chain Monte Carlo scheme for this hierarchical framework requires the spatial effects to be sampled from their full conditional posteriors one-by-one resulting in poor mixing. More importantly, it makes the model computationally inefficient for datasets with large number of units. In this article, we propose a Bayesian approach that uses the spectral structure of the adjacency to construct a low-rank expansion for modeling spatial dependence. We propose a pair of computationally efficient estimati