Asymmetric author-topic model for knowledge discovering of big data in toxicogenomics.

ABSTRACT: The advancement of high-throughput screening technologies facilitates the generation of massive amount of biological data, a big data phenomena in biomedical science. Yet, researchers still heavily rely on keyword search and/or literature review to navigate the databases and analyses are often done in rather small-scale. As a result, the rich information of a database has not been fully utilized, particularly for the information embedded in the interactive nature between data points that are largely ignored and buried. For the past 10 years, probabilistic topic modeling has been recognized as an effective machine learning algorithm to annotate the hidden thematic structure of massive collection of documents. The analogy between text corpus and large-scale genomic data enables the applicati

SUBMITTER: Chung MH 

PROVIDER: S-EPMC4403303 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature