Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Motivation: Reverse engineering of genetic regulatory networks from experimental data is the first step toward the modeling of genetic networks. Linear state-space models, also known as linear dynamical models, have been applied to model genetic networks from gene expression time series data, but existing works have not taken into account available structural information. Without structural constraints, estimated models may contradict biological knowledge and estimation methods may over-fit.#R##N##R##N#Results: In this report,  extended expectation-maximization (EM) algorithms to incorporate prior network structure and to estimate genetic regulatory networks that can track and predict gene expression profiles.  applied our method to synthetic data and to SOS data and shod that our method significantly outperforms the regular EM without structural constraints.#R##N##R##N#Availability: The Matlab code is available upon request and the SOS data can be downloaded from http://www.izmann.ac.il/mcb/UriAlon/Papers/SOSData/, courtesy of Uri Alon. Zak's data is available from his bsite, http://www.che.udel.edu/systems/people/zak#R##N##R##N#Contact: hxiong@cs.tamu.edu#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online.
['Hao Xiong', 'Yoonsuck Choe']
I am writing a research paper on a idea Structural systems identification of genetic regulatory networks
[{"from": "human", "value": "I am writing a research paper on a idea Structural systems identification of genetic regulatory 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: Reverse engineering of genetic regulatory networks from experimental data is the first step toward the modeling of genetic networks. Linear state-space models, also known as linear dynamical models, have been applied to model genetic networks from gene expression time series data, but existing works have not taken into account available structural information. Without structural constraints, estimated models may contradict biological knowledge and estimation methods may over-fit.#R##N##R##N#Results: In this report,  extended expectation-maximization (EM) algorithms to incorporate prior network structure and to estimate genetic regulatory networks that can track and predict gene expression profiles.  applied our method to synthetic data and to SOS data and shod that our method significantly outperforms the regular EM without structural constraints.#R##N##R##N#Availability: The Matlab code is available upon request and the SOS data can be downloaded from http://www.izmann.ac.il/mcb/UriAlon/Papers/SOSData/, courtesy of Uri Alon. Zak's data is available from his bsite, http://www.che.udel.edu/systems/people/zak#R##N##R##N#Contact: hxiong@cs.tamu.edu#R##N##R##N#Supplementary information: Supplementary data are available at Bioinformatics online."}]