Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2014 (2014), Article ID 170289, 10 pages
http://dx.doi.org/10.1155/2014/170289
Research Article

Clinic-Genomic Association Mining for Colorectal Cancer Using Publicly Available Datasets

1Department of Biomedical Engineering, Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China
2General Hospital of Ningxia Medical University, Yinchuan 750004, China

Received 30 March 2014; Accepted 12 May 2014; Published 2 June 2014

Academic Editor: Degui Zhi

Copyright © 2014 Fang Liu 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 provides the following results:

Table S1 lists the filter rule used to search GEO for colorectal cancer related GSE. Table S2 lists the colorectal cancer related keywords used to search GAD. Table S3 list clinical related semantic types used to screen out clinical concepts. Table S4 presents the source code of modified logarithmic scale detection algorithm in MATLAB. Table S5 list the top 10 pathways with the most number of genes. Table S6 lists all the colorectal cancer related clinical concepts found by the proposed method. Table S7 lists all the colorectal cancer related genes found by the proposed method. Table S8 lists all the clinic-genomic associations mined out by the proposed method.

Figure S1 shows the concept distribution against semantic types. Figure S2~S17 are Gephi outputs of clinic-genomic associations classified by semantic types.

  1. Supplementary Material