Notice of Violation of IEEE Publication Principles: Systematically Identifying Genes and Pathways in Multiple Cancer Types Using HGD & PSO-SVM

Notice of Violation of IEEE Publication Principles

"Systematically Identifying Genes and Pathways in Multiple Cancer Types Using HGD & PSO-SVM"
by Mahesh Visvanathan, Adagarla Bhargav Srinivas, Gerald Lushington and Sitta Sittapalam
in the Proceedings of the 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing, August 2009, pp. 494-497

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

This paper contains significant portions of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission. The committee also concluded that Adagarla Bhargav Srinivas was not aware or responsible for the misconduct.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

"PathExpress: A Web-based Tool to Identify Relevant Pathways in Gene Expression Data"
by Nicolas Goffard and Georg Weiller
in Nucleic Acids Research, Vol 35, Web Server Issue, Oxford, June 2007, pp. W176-W181

"A Novel Feature Selection for Gene Expression Data"
by Li-Yeh Chaung, Cheng-Hong Yang, Chung-Jui Tu, Cheng-Huei Yang
in the Proceedings of the Joint Conference on Information Sciences, Atlantis Press October 2006

"A Hybrid Feature Selection Method for Microarray Classification"
by Cheng-San Yang, Li-yeh Chuang, Chao-Hsuan Ke, Cheng-Hong Yang
in the International Journal of Computer Science, Vol 35, Issue 3, International Association of Engineers, August 2008


Identification of genes and pathways which are risk factors for multiple cancers could help us prevent or treat cancer more effectively. Machine learning techniques have been extensively used to analyze microarray data but, most methods are based on the identification of significant associations of gene ontology terms with groups of genes. This does not directly reflect metabolic networks. A more systematic approach is considered. As a first step, we did pathway analysis using Hyper Geometric Distribution (HGD) and significantly overrepresented sets of reactions (pathways or sub-pathways) were identified. As a second step, feature selection based Particle Swarm Optimization (PSO) and the K-Nearest Neighbor (K-NN) methods were used. We also used the Leave-One-Out Cross-Validation (LOOCV) as an evaluator of PSO and One-Versus-Rest (OVR) method as a component classifier. Experimental results show that our method simplifies features effectively and obtains higher classification accuracy than the other classification methods from the literature.