Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: The noisy nature of transcriptomic data hinders the biological relevance of conventional network centrality measures, often used to select gene candidates in co-expression networks. Therefore, new tools and methods are required to improve the prediction of mechanistically important transcriptional targets. Results:  propose an original network centrality measure, called annotation transcriptional centrality (ATC) computed by integrating gene expression profiles from microarray experiments with biological knowledge extracted from public genomic databases. ATC computation algorithm delimits representative functional domains in the co-expression network and then relies on this information to find key nodes that modulate propagation of functional influences within the network.  demonstrate ATC ability to predict important genes in several experimental models and provide improved biological relevance over conventional topological network centrality measures. Availability: ATC computational routine is implemented in a publicly available tool named FunNet (www.funnet.info) Contact: edi.prifti@crc.jussieu.fr Supplementary information: Supplementary data are available at Bioinformatics online.
['Edi Prifti', 'Jean-Daniel Zucker', 'Karine Clément', 'Corneliu Henegar']
I am writing a research paper on a idea Interactional and functional centrality in transcriptional co-expression networks
[{"from": "human", "value": "I am writing a research paper on a idea Interactional and functional centrality in transcriptional co-expression networks"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: The noisy nature of transcriptomic data hinders the biological relevance of conventional network centrality measures, often used to select gene candidates in co-expression networks. Therefore, new tools and methods are required to improve the prediction of mechanistically important transcriptional targets. Results:  propose an original network centrality measure, called annotation transcriptional centrality (ATC) computed by integrating gene expression profiles from microarray experiments with biological knowledge extracted from public genomic databases. ATC computation algorithm delimits representative functional domains in the co-expression network and then relies on this information to find key nodes that modulate propagation of functional influences within the network.  demonstrate ATC ability to predict important genes in several experimental models and provide improved biological relevance over conventional topological network centrality measures. Availability: ATC computational routine is implemented in a publicly available tool named FunNet (www.funnet.info) Contact: edi.prifti@crc.jussieu.fr Supplementary information: Supplementary data are available at Bioinformatics online."}]