Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Computational inference methods that make use of graphical models to extract regulatory networks from gene expression data can have difficulty reconstructing dense regions of a network, a consequence of both computational complexity and unreliable parameter estimation when sample size is small. As a result, identification of hub genes is of special difficulty for these methods.#R##N##R##N#Methods:  present a new algorithm, Empirical Light Mutual Min (ELMM), for large network reconstruction that has properties ll suited for recovery of graphs with high-degree nodes. ELMM reconstructs the undirected graph of a regulatory network using empirical Bayes conditional independence testing with a heuristic relaxation of independence constraints in dense areas of the graph. This relaxation allows only one gene of a pair with a putative relation to be aware of the network connection, an approach that is aimed at easing multiple testing problems associated with recovering densely connected structures.#R##N##R##N#Results: Using in silico data,  show that ELMM has better performance than commonly used network inference algorithms including GeneNet, ARACNE, FOCI, GENIE3 and GLASSO.  also apply ELMM to reconstruct a network among 5492 genes expressed in human lung airway epithelium of healthy non-smokers, healthy smokers and individuals with chronic obstructive pulmonary disease assayed using microarrays. The analysis identifies dense sub-networks that are consistent with known regulatory relationships in the lung airway and also suggests novel hub regulatory relationships among a number of genes that play roles in oxidative stress and secretion.#R##N##R##N#Availability and implementation: Software for running ELMM is made available at http://mezeylab.cb.bscb.cornell.edu/Software.aspx.#R##N##R##N#Contact: ramimahdi@yahoo.com or jgm45@cornell.edu#R##N##R##N#Supplementary information:Supplementary data are available at Bioinformatics online.
['Rami N. Mahdi', 'Abishek Sainath Madduri', 'Guoqing Wang', 'Yael Strulovici-Barel', 'Jacqueline Salit', 'Neil R. Hackett', 'Ronald G. Crystal', 'Jason G. Mezey']
I am writing a research paper on a idea Empirical Bayes conditional independence graphs for regulatory network recovery
[{"from": "human", "value": "I am writing a research paper on a idea Empirical Bayes conditional independence graphs for regulatory network recovery"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Computational inference methods that make use of graphical models to extract regulatory networks from gene expression data can have difficulty reconstructing dense regions of a network, a consequence of both computational complexity and unreliable parameter estimation when sample size is small. As a result, identification of hub genes is of special difficulty for these methods.#R##N##R##N#Methods:  present a new algorithm, Empirical Light Mutual Min (ELMM), for large network reconstruction that has properties ll suited for recovery of graphs with high-degree nodes. ELMM reconstructs the undirected graph of a regulatory network using empirical Bayes conditional independence testing with a heuristic relaxation of independence constraints in dense areas of the graph. This relaxation allows only one gene of a pair with a putative relation to be aware of the network connection, an approach that is aimed at easing multiple testing problems associated with recovering densely connected structures.#R##N##R##N#Results: Using in silico data,  show that ELMM has better performance than commonly used network inference algorithms including GeneNet, ARACNE, FOCI, GENIE3 and GLASSO.  also apply ELMM to reconstruct a network among 5492 genes expressed in human lung airway epithelium of healthy non-smokers, healthy smokers and individuals with chronic obstructive pulmonary disease assayed using microarrays. The analysis identifies dense sub-networks that are consistent with known regulatory relationships in the lung airway and also suggests novel hub regulatory relationships among a number of genes that play roles in oxidative stress and secretion.#R##N##R##N#Availability and implementation: Software for running ELMM is made available at http://mezeylab.cb.bscb.cornell.edu/Software.aspx.#R##N##R##N#Contact: ramimahdi@yahoo.com or jgm45@cornell.edu#R##N##R##N#Supplementary information:Supplementary data are available at Bioinformatics online."}]