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Arzucan, O., Thuy, V., Erkan, G. and Radev, D.R. (2008) Identifying Gene-Disease Associations Using Centrality on a Literature Mined Gene-Interaction Network. Bioinformatics, 24, i277-i285.
https://doi.org/10.1093/bioinformatics/btn182

has been cited by the following article:

• TITLE: Identification of Inference Genes in Breast Cancer Network

  AUTHORS: Keilash Chirom, Shahnawaz Ali, Md. Zubbair Malik, Romana Ishrat, R. K. Brojen Singh

  KEYWORDS: Breast Cancer, Network Theory, Inference Genes, Topological Propreties, Centrality

  JOURNAL NAME: Journal of Biosciences and Medicines, Vol.5 No.9, August 29, 2017

  ABSTRACT: Complex breast cancer network constructed from experimentally verified seventy genes, by coordinating standard seven human protein and genome databases, follows hierarchical scale free features. Centrality based method of identification of inferred genes is implemented to this network and has predicted forty nine breast cancer genes, and nineteen non-breast cancer genes. As predicting good candidate genes before experimental analysis will save time and effort both. Fourteen genes out of nineteen are found to involve in various types of cancer and diseases, and five genes are engaged in non-cancer diseases. Some of the inferred genes need proper experimental investigation to understand fundamental roles of these genes in regulating breast cancer network.