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BioMed Research International
Volume 2013 (2013), Article ID 304029, 8 pages
http://dx.doi.org/10.1155/2013/304029
Research Article

Prediction and Analysis of Retinoblastoma Related Genes through Gene Ontology and KEGG

1Department of Ophthalmology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China
2Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
3State Key Laboratory of Medical Genomics, Institute of Health Sciences, Shanghai Jiaotong University School of Medicine and Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China
4College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
5Department of Ophthalmology, Shanghai First People’s Hospital, Shanghai Jiaotong University, Shanghai 200080, China
6Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York City, NY 10029, USA

Received 17 June 2013; Accepted 16 July 2013

Academic Editor: Yudong Cai

Copyright © 2013 Zhen Li 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

Supplementary Material includes Supplementary S1: This file includes two sheets. The first one shows the differentially expressed genes in two previous RB studies and the 119 overlap RB genes. The second sheet shows all the positive samples and negative samples used in this study. The ration between positive and negative samples is 1:50. The corresponding Ensembl protein IDs were given. Supplementary S2: This file contains twenty sheets. For each dataset, there were two tables, of which one is MaxRel feature table ranked according to the relevance between the features and the class of the samples and the other one is the mRMR feature table ranked according to the redundancy and relevance to the features of the samples. Supplementary S3: The sensitivity (Sn), specificity (Sp), accuracy (Ac), Matthews’s correlation coefficient (MCC) generated by each run of the IFS for each of the ten datasets. Supplementary S4: The final optimal 1069 features including 1061 GO terms and 8 KEGG pathways. Supplementary S5: IFS curves for the rest nine datasets. Supplementary S6: GO and KEGG enrichment result for 119 RB genes

  1. Supplementary Material 1
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  6. Supplementary Material 6