Purpose The traditional drug development process is costly, time consuming and risky. Using computational methods to discover drug repositioning opportunities is a promising and efficient strategy in the era of big data. The explosive growth of large-scale genomic, phenotypic data and all kinds...

In this paper the authors build on prior literature to develop an adaptive and time-varying metadata-enabled dynamic topic model (mDTM) and apply it to a large Weibo dataset using an online Gibbs sampler for parameter estimation. Their approach simultaneously captures the maximum number of...

We develop a model wherein a risk-neutral but ambiguity-averse principal contracts with a risk-averse agent who has a risky project. Both the agent and the principal can observe the project output and a public signal. The correlation between the output and the public signal is private...