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

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

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...

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Bibliographic Details
Main Authors: Ming-Hua eChung, Yuping eWang, Hailin eTang, Wen eZou, John eBasinger, Xiaowei eXu, Weida eTong
Format: Article
Language:English
Published: Frontiers Media S.A. 2015-04-01
Series:Frontiers in Pharmacology
Subjects:
bioinformatics
machine learning
toxicogenomics
author-topic model
probabilistic topic modeling
TG-GATEs
Online Access:http://journal.frontiersin.org/Journal/10.3389/fphar.2015.00081/full

Internet

http://journal.frontiersin.org/Journal/10.3389/fphar.2015.00081/full

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