Authors: Vikram Kalluru; Raghu Machiraju; Kun Huang
Addresses: Department of Electrical Engineering, The Ohio State University, Columbus, OH 43210, USA ' Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA ' Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA; OSUCCC Biomedical Informatics Shared Resource, The Ohio State University, Columbus, OH 43210, USA
Abstract: Since co-expressed genes often are co-regulated by a group of transcription factors, different conditions (e.g. disease versus normal) may lead to different transcription factor activities and therefore different co-expression networks. We propose a method for identifying condition-specific co-expression networks by combining our recently developed network quasi-clique mining algorithm and the expected conditional F-statistic. We apply this method to compare the transcriptional programmes between the non-basal and basal types of breast cancers. The results provide a new perspective for studying gene interaction dynamics in cancers and assessing the effects of perturbation on key genes such as transcription factors. Our work is a way for dynamically characterising the gene interaction networks.
Keywords: gene co-expression; co-expression networks; expected conditional F-statistic; quasi-clique mining; breast cancer; gene interaction dynamics; cancers; perturbation; transcription factors; gene interaction networks.
International Journal of Computational Biology and Drug Design, 2013 Vol.6 No.1/2, pp.50 - 59
Published online: 20 Feb 2013 *Full-text access for editors Access for subscribers Purchase this article Comment on this article