Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new inference approach to general dynamic models of gene regulatory networks (GRN) is introduced. The methodology is based on a Maximum a Posteriori (MAP) smoothing of time series data from which mean field variables of the dynamics are estimated. The interactions are modeled by a Continuous Time Recurrent Neural Network (CTRNN). Parameter estimation of the CTRNN is performed without the need to numerically solve the system of nonlinear differential equations. The method is tested on a benchmark of real genetic networks and displays superior performance, in terms of the mean squared error of the expression dynamics, compared to other formalisms. HighlightsThe methods requires lesser function evaluations than previous approaches.The procedure reproduces the behavior of the variables of the actual dynamical system.The method is competitive in the dynamic reconstruction from genetic expression time series.
['Arturo Berrones', 'Edgar Benjamin Montúfar Jimenéz', 'María Aracelia Alcorta-García', 'F-Javier Almaguer', 'Brenda Peña']
I am writing a research paper on a idea Parameter inference of general nonlinear dynamical models of gene regulatory networks from small and noisy time series
[{"from": "human", "value": "I am writing a research paper on a idea Parameter inference of general nonlinear dynamical models of gene regulatory networks from small and noisy time series"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: A new inference approach to general dynamic models of gene regulatory networks (GRN) is introduced. The methodology is based on a Maximum a Posteriori (MAP) smoothing of time series data from which mean field variables of the dynamics are estimated. The interactions are modeled by a Continuous Time Recurrent Neural Network (CTRNN). Parameter estimation of the CTRNN is performed without the need to numerically solve the system of nonlinear differential equations. The method is tested on a benchmark of real genetic networks and displays superior performance, in terms of the mean squared error of the expression dynamics, compared to other formalisms. HighlightsThe methods requires lesser function evaluations than previous approaches.The procedure reproduces the behavior of the variables of the actual dynamical system.The method is competitive in the dynamic reconstruction from genetic expression time series."}]