title: Identify condition-specific gene co-expression networks.
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.
authors: Department of Electrical Engineering, The Ohio State University, Columbus, OH 43210, USA. kalluru.1@osu.edu
authors: Vikram
authors: V
authors: Kalluru
authors: Raghu
authors: R
authors: Machiraju
authors: Kun
authors: K
authors: Huang
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism; D030541:Databases, Genetic; D005260:Female; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D005796:Genes / Q000235:genetics / Q000502:physiology; D023281:Genomics / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis; D011506:Proteins / Q000032:analysis / Q000145:classification / Q000235:genetics / Q000378:metabolism
chemical_list: D011506:Proteins