Xu Ling

TL;DR: The proposed Topic-Sentiment Mixture (TSM) model can reveal the latent topical facets in a Weblog collection, the subtopics in the results of an ad hoc query, and their associated sentiments and could also provide general sentiment models that are applicable to any ad hoc topics.

TL;DR: A very efficient algorithm based on a 'gene teams' model that allows genes in the clusters to appear in different orders allows us to detect conserved gene clusters under flexible evolutionary constraints in a large number of genomes.

TL;DR: Empirical experiments show that the approach can effectively generate a multi-faceted overview for arbitrary topics; the generated overviews are comparable with those generated by supervised methods with training examples and are also more informative than unstructured flat summaries.

TL;DR: A probabilistic model of gains and losses of TFBSs, a process being recognized as an important part of regulatory sequence evolution is presented, and a computational framework that uses this model to solve the problems of CRM alignment and prediction is presented.