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BioMed Research International
Volume 2015, Article ID 964795, 8 pages
http://dx.doi.org/10.1155/2015/964795
Research Article

Identification of Novel Thyroid Cancer-Related Genes and Chemicals Using Shortest Path Algorithm

1Department of Surgery, China-Japan Union Hospital of Jilin University, Changchun 130033, China
2The Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China

Received 20 September 2014; Accepted 5 December 2014

Academic Editor: Tao Huang

Copyright © 2015 Yang Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Supplementary Material

The Supplementary Material contains six files. In detail, Online Supporting Information S1 lists thyroid cancer-related genes and chemicals; Online Supporting Information S2 lists 636 candidate genes and 174 candidate chemicals; Online Supporting Information S3 lists 169 significant candidate genes and 49 significant candidate chemicals; Online Supporting Information S4 lists KEGG enrichment results of 169 significant candidate genes; Online Supporting Information S5 lists GO enrichment results of 169 significant candidate genes; Online Supporting Information S6 lists the discussion of 29 significant candidate chemicals.

  1. Supplementary Material