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BioMed Research International
Volume 2014 (2014), Article ID 450386, 10 pages
Research Article

Gene Ontology and KEGG Enrichment Analyses of Genes Related to Age-Related Macular Degeneration

1Department of Ophthalmology, Shanghai First People’s Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200080, China
2Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai First People’s Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200080, China
3The Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Jiaotong University School of Medicine and Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200025, China
4Institute of Systems Biology, Shanghai University, Shanghai 200444, China
5College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China

Received 13 June 2014; Accepted 21 July 2014; Published 6 August 2014

Academic Editor: Tao Huang

Copyright © 2014 Jian Zhang 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.


Identifying disease genes is one of the most important topics in biomedicine and may facilitate studies on the mechanisms underlying disease. Age-related macular degeneration (AMD) is a serious eye disease; it typically affects older adults and results in a loss of vision due to retina damage. In this study, we attempt to develop an effective method for distinguishing AMD-related genes. Gene ontology and KEGG enrichment analyses of known AMD-related genes were performed, and a classification system was established. In detail, each gene was encoded into a vector by extracting enrichment scores of the gene set, including it and its direct neighbors in STRING, and gene ontology terms or KEGG pathways. Then certain feature-selection methods, including minimum redundancy maximum relevance and incremental feature selection, were adopted to extract key features for the classification system. As a result, 720 GO terms and 11 KEGG pathways were deemed the most important factors for predicting AMD-related genes.