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BioMed Research International
Volume 2013 (2013), Article ID 870795, 7 pages
http://dx.doi.org/10.1155/2013/870795
Research Article

Prediction of Gene Phenotypes Based on GO and KEGG Pathway Enrichment Scores

1Institute of Systems Biology, Shanghai University, 99 ShangDa Road, Shanghai 200444, China
2State 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 200025, China
3College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
4College of Life Science, Shanghai University, 99 ShangDa Road, Shanghai 200444, China

Received 19 August 2013; Accepted 23 September 2013

Academic Editor: Tao Huang

Copyright © 2013 Tao 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.

Abstract

Observing what phenotype the overexpression or knockdown of gene can cause is the basic method of investigating gene functions. Many advanced biotechnologies, such as RNAi, were developed to study the gene phenotype. But there are still many limitations. Besides the time and cost, the knockdown of some gene may be lethal which makes the observation of other phenotypes impossible. Due to ethical and technological reasons, the knockdown of genes in complex species, such as mammal, is extremely difficult. Thus, we proposed a new sequence-based computational method called kNNA-based method for gene phenotypes prediction. Different to the traditional sequence-based computational method, our method regards the multiphenotype as a whole network which can rank the possible phenotypes associated with the query protein and shows a more comprehensive view of the protein's biological effects. According to the prediction result of yeast, we also find some more related features, including GO and KEGG information, which are making more contributions in identifying protein phenotypes. This method can be applied in gene phenotype prediction in other species.