Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Genetic variants identified by genome-wide association studies to date explain only a small fraction of total heritability. Gene-by-gene interaction is one important potential source of unexplained total heritability.  propose a novel approach to detect such interactions that uses penalized regression and sparse estimation principles, and incorporates outside biological knowledge through a network-based penalty.#R##N##R##N#Results:  tested our new method on simulated and real data. Simulation shod that with reasonable outside biological knowledge, our method performs noticeably better than stage-wise strategies (i.e. selecting main effects first, and interactions second, from those main effects selected) in finding true interactions, especially when the marginal strength of main effects is ak.  applied our method to Framingham Heart Study data on total plasma immunoglobulin E (IgE) concentrations and found a number of interactions among different classes of human leukocyte antigen genes that may interact to influence the risk of developing IgE dysregulation and allergy.#R##N##R##N#Availability: The proposed method is implemented in R and available at http://math.bu.edu/people/kolaczyk/software.html.#R##N##R##N#Contact: chenlu@bu.edu#R##N##R##N#Supplementary information:Supplementary data are available at Bioinformatics online.
['Chen Lu', 'Jeanne Latourelle', 'George T. O’Connor', 'Josée Dupuis', 'Eric D. Kolaczyk']
I am writing a research paper on a idea Network-guided sparse regression modeling for detection of gene-by-gene interactions
[{"from": "human", "value": "I am writing a research paper on a idea Network-guided sparse regression modeling for detection of gene-by-gene interactions"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Genetic variants identified by genome-wide association studies to date explain only a small fraction of total heritability. Gene-by-gene interaction is one important potential source of unexplained total heritability.  propose a novel approach to detect such interactions that uses penalized regression and sparse estimation principles, and incorporates outside biological knowledge through a network-based penalty.#R##N##R##N#Results:  tested our new method on simulated and real data. Simulation shod that with reasonable outside biological knowledge, our method performs noticeably better than stage-wise strategies (i.e. selecting main effects first, and interactions second, from those main effects selected) in finding true interactions, especially when the marginal strength of main effects is ak.  applied our method to Framingham Heart Study data on total plasma immunoglobulin E (IgE) concentrations and found a number of interactions among different classes of human leukocyte antigen genes that may interact to influence the risk of developing IgE dysregulation and allergy.#R##N##R##N#Availability: The proposed method is implemented in R and available at http://math.bu.edu/people/kolaczyk/software.html.#R##N##R##N#Contact: chenlu@bu.edu#R##N##R##N#Supplementary information:Supplementary data are available at Bioinformatics online."}]