Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Results:  have developed LeTICE (Learning Transcriptional networks from the Integration of ChIP–chip and Expression data), an algorithm for learning a transcriptional network from ChIP–chip and expression data. The network is specified by a binary matrix of transcription factor (TF)–gene interactions partitioning genes into modules and a background of genes that are not involved in the transcriptional regulation.  define a likelihood of a network, and then search for the network optimizing the likelihood.#R##N##R##N# applied LeTICE to the location and expression data from yeast cells grown in rich media to learn the transcriptional network specific to the yeast cell cycle. It found 12 condition-specific TFs and 15 modules each of which is highly represented with functions related to particular phases of cell-cycle regulation.#R##N##R##N#Availability: Our algorithm is available at http://linus.nci.nih.gov/Data/YounA/LeTICE.zip#R##N##R##N#Contact: youna2@mail.nih.gov#R##N##R##N#Supplementary Information:Supplementary data are available at Bioinformatics online.
['Ahrim Youn', 'David J. Reiss', 'Werner Stuetzle']
I am writing a research paper on a idea Learning transcriptional networks from the integration of ChIP–chip and expression data in a non-parametric model
[{"from": "human", "value": "I am writing a research paper on a idea Learning transcriptional networks from the integration of ChIP\u2013chip and expression data in a non-parametric model"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Results:  have developed LeTICE (Learning Transcriptional networks from the Integration of ChIP\u2013chip and Expression data), an algorithm for learning a transcriptional network from ChIP\u2013chip and expression data. The network is specified by a binary matrix of transcription factor (TF)\u2013gene interactions partitioning genes into modules and a background of genes that are not involved in the transcriptional regulation.  define a likelihood of a network, and then search for the network optimizing the likelihood.#R##N##R##N# applied LeTICE to the location and expression data from yeast cells grown in rich media to learn the transcriptional network specific to the yeast cell cycle. It found 12 condition-specific TFs and 15 modules each of which is highly represented with functions related to particular phases of cell-cycle regulation.#R##N##R##N#Availability: Our algorithm is available at http://linus.nci.nih.gov/Data/YounA/LeTICE.zip#R##N##R##N#Contact: youna2@mail.nih.gov#R##N##R##N#Supplementary Information:Supplementary data are available at Bioinformatics online."}]